<section id="isPasted">When early humans learned to harness fire for heat around 400,000 years ago, it marked a monumental turning point in mankind’s history. Heat is used for various purposes in the modern age, particularly in manufacturing, including refining, steel, cement, pulp and paper. The fossil fuels used to generate heat in these manufacturing sectors generate substantial greenhouse gas emissions. Hence, the United States Department of Energy (DOE) is now heavily investing in alternative technologies for heat generation. Under the Manufacturing USA initiative, the DOE has made a significant stride by awarding $70 million to the&nbsp;<a href="https://epixc.org/">Electrified Processes for Industry without Carbon (EPIXC) Institute</a>. This institute, led by Arizona State University and with key partnerships with national labs and universities, including Texas A&amp;M University, is a beacon of hope for the future of electrified heating technologies. EPIXC's funding, through the Office of Energy Efficiency and Renewable Energy’s Industrial Efficiency and Decarbonization Office, is a testament to the promising future of these technologies. “A significant portion of the heat used in manufacturing is currently generated by burning fossil fuels,” explained Dr. Mark Barteau, professor in the Artie McFerrin Department of Chemical Engineering and C. D. Holland ’53 Chair at Texas A&amp;M. “However, with the potential of abundant carbon-free energy in the future, we could revolutionize the industry by using electrical energy for heating, thereby significantly reducing greenhouse gas emissions.” Texas A&amp;M is a core partner in EPIXC through the leadership of the&nbsp;<a href="https://energy.tamu.edu/">Texas A&amp;M Energy Institute</a>. Texas A&amp;M researchers will contribute across the EPIXC mission, including development of a “roadmap” for decarbonization of manufacturing processes. The five-year program will be initiated with a two-year “jumpstart” project devoted to developing innovative electrified heating technologies for propane dehydrogenation, a chemical process for converting propane to propylene, a material that has several commercial applications, such as producing plastics, solvents and other chemicals. As the name suggests, propane dehydrogenation involves the selective removal of two hydrogen atoms from a propane feedstock to make propylene. This reaction takes place in the presence of catalysts to speed up the reaction. But propane dehydrogenation is an endothermic reaction, which means that the chemical reaction consumes heat, which currently comes from burning fossil fuels. With their funding, Barteau and his team aim to (1) develop electrified chemical processes and (2) establish a program for workforce development for manufacturing personnel and researchers. His team, including Dr. Micah Green and Dr. Benjamin Willhite both professors in the chemical engineering department, is currently developing a reactor based on internal radiofrequency (RF) heating of the propane dehydrogenation catalyst. &nbsp;A reason for using RF is to achieve good radiation penetration depth; that is, the radiation can reach inside the chemical reactor and create uniform heating temperatures. Thus, the team will work on reactor designs that best match the source of electrical energy put into the system.</section><blockquote>A significant portion of the heat used in manufacturing is currently generated by burning fossil fuels. However, with the potential of abundant carbon-free energy in the future, we could revolutionize the industry by using electrical energy for heating, thereby significantly reducing greenhouse gas emissions.<cite>- Dr. Mark Barteau</cite></blockquote><section>Another challenge that Barteau will be addressing is scaling these technologies from the laboratory to pilot-scale demonstrations and, ultimately, deployment in the field.&nbsp;“A world-scale propane dehydrogenation plant will make about 600,000 metric tons of propylene per year. Electrified processes will need to be operable at that scale since these plants will require large amounts of clean electricity,” said Barteau. “This energy could be generated on-site, or it could be generated elsewhere and transmitted.” The project could reduce CO2 emissions by 0.3 metric tons per metric ton of propylene produced and increase propylene yields by up to 10%. The team will be further strengthened by the participation of two key members from the Texas A&amp;M Energy Institute leadership: Dr. Stratos Pistikopoulos, professor of chemical engineering and director of the Texas A&amp;M Energy Institute, Dr. Konstantinos Pappas, associate director at the energy institute. Their expertise will significantly contribute to the development of training curricula for workforce development, drawing upon the institute’s modular concept of its renowned educational program. As climate change and global warming threaten the planet, Texas A&amp;M and partners at EPIXC will strive to reduce the dependence on fossil fuels for heat generation and decarbonize industries by developing technologies to electrify process heating. The successful implementation of these electrified processes could significantly reduce greenhouse gas emissions from manufacturing, contributing to a more sustainable and environmentally friendly industrial sector. Funding for this research is administered by the Texas A&amp;M Engineering Experiment Station (TEES), the official research agency for Texas A&amp;M Engineering.</section>

Texas A&M researchers will conduct a two-year “jumpstart” project devoted to developing electrified heating technologies.

Moving Toward an Electrified Solution to Generate Heat for Manufacturing Industries

Magnetic cores are essential in modern electronics, influencing the functionality and efficiency of devices, from power supplies to renewable energy systems. Traditional designs, especially those with a single large air gap, struggle to meet the high-frequency and efficiency demands of present-day applications.&nbsp;This challenge has catalyzed the development of distributed air gap cores, which are primarily used to increase power handling capabilities, enable higher currents, and enhance the efficiency and thermal performance of magnetics. Historically, the technology behind distributed air gap cores has been around for nearly 30 years but has always been an expensive, highly customized solution –that is, until now.&nbsp;Today, <a href="https://www.tdk-electronics.tdk.com/en/2796388/products/product-catalog/ferrites-and-accessories/cores-with-distributed-air-gaps" target="_blank" rel="noopener noreferrer">TDK Corporation</a>, a global leader in electronic components, is innovating the standardization of Distributed air gap cores in a way that is revolutionizing how the market will specify, purchase, and adopt these crucial components. This article explores how distributed air gap cores are revolutionizing magnetic core design, offering a glimpse into their principles, advantages, and standardization efforts led by TDK.<h2 dir="ltr" id="isPasted">Challenges of Traditional Single Large Air Gap Cores</h2>Traditional magnetic cores with single large air gaps exhibit a number of drawbacks due to their high magnetic reluctance. This reluctance acts as resistance to magnetization, leading to considerable energy losses as magnetic fields stray from their intended paths. The large fringing flux emitted from these air gaps spreads into the winding area, causing copper losses that further lead to a rise in temperature and proximity losses in high-frequency magnetics.These compounded inefficiencies escalate into several challenges, notably excessive heat that degrades component performance and shortens device lifespan—critical issues in high-power and high-frequency applications. Additionally, the need to mitigate these effects results in larger, heavier cores, posing significant limitations in applications requiring compactness and device miniaturization.<h2 dir="ltr">Development of Distributed Air Gap Cores</h2>Distributed air gap cores are a type of magnetic core, which feature a magnetic material interspersed with non-magnetic gaps. These gaps are distributed uniformly throughout the material, rather than being concentrated in one area. By integrating multiple smaller air gaps throughout the core, distributed air gap technology optimizes the magnetic flux path, significantly reducing areas prone to flux leakage and fringing effects. This strategic distribution of air gaps minimizes non-functional magnetic fields and enhances the core's overall efficiency and performance.Additionally, this configuration allows for a more compact core design without sacrificing power handling capabilities, thus supporting the trend towards miniaturization in technology. The benefit extends to improved thermal management, as the reduced energy losses lead to less heat generation, allowing devices to operate at higher frequencies without compromising their longevity or performance.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzIzNTQ4MzA2NDk5LWltYWdlMy5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 100%px;" class="fr-fic fr-dib" alt="thermal-image-temperature-comparision-single-air-ga-core-vs-distributed-air-gap-core">&nbsp;A thermal image comparison shows that after 60 minutes of operation at 3.4 A and 50 kHz, the temperature increase in a single large air gap core (left) is significantly higher than in a distributed air gap core (right).Here is a summary of the key benefits of using distributed air gap cores to address issues posed by their traditional counterparts:<ul><li dir="ltr">Improved Fringing Flux: Anywhere a gap is present, fringing flux is emitted. When there is a larger gap, there is also a larger fringing flux path. By incorporating a distributed air gap, the fringing flux losses decrease as the number of smaller air gaps increases. This can reduce the fringing flux losses by up to 70%.&nbsp;</li><li dir="ltr">Increased Power Density and Optimized Efficiency: Distributed air gaps increase the power handling capability of the design. In many cases the winding area is reduced to avoid fringing flux losses. By using distributed gapped cores to minimize the fringing flux, the full winding area can be used. This can allow for either a smaller component or higher power through the originally selected component.</li><li dir="ltr">Enhanced Performance: In high-frequency applications, the skin effect and proximity effect can lead to significant losses in electrical conductors. The skin effect refers to the tendency of alternating current (AC) to distribute itself within a conductor such that the current density is highest near the surface, effectively reducing the cross-sectional area available for current flow. The proximity effect, on the other hand, occurs when alternating magnetic fields from neighboring conductors cause the current to concentrate in certain regions of a conductor, increasing resistance and associated losses.&nbsp; Litz wire is a specialized type of wire designed to mitigate these effects. It consists of multiple thin, insulated strands woven together in a specific pattern, which helps reduce the losses caused by the skin and proximity effects. However, when using distributed air gap cores, which reduce the effects of fringing flux, it is possible to use solid wire instead of Litz wire. This substitution could lead to higher copper utilization within the winding area, thereby increasing efficiency and reducing costs.</li><li dir="ltr">Utilization of New Semiconductor Material: Distributed air gap cores are well-suited for applications using higher frequency switching technologies like Silicon Carbide and Gallium Nitrides. Their design reduces energy losses and enhances thermal management, making them ideal for handling the increased power densities and rapid switching speeds these technologies require.</li></ul><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1723548324218-image2.gif" style="width: 100%px;" class="fr-fic fr-dib" alt="hot-spot-comparision-single-gap-vs-distributed-gap-core">Hot spot comparison for single large air gap vs. distributed air gap cores: distributed air gaps reduce fringing flux and hotspots, improving thermal performance and efficiencyDistributed air gap cores have seen significant innovation in recent years. However, these solutions have been tailored and delivered specifically for the design of each end application.&nbsp;An unfortunate side effect of this is that each time a core is created for a custom application, it likely will not be used again for another design, increasing the overall cost of the technology and preventing new innovations from being passed on to others.<h2 dir="ltr">Introducing TDK’s Standardized Solutions</h2>TDK has responded to the need for taking a highly customized technology like the distributed air gap core and <a href="https://www.tdk-electronics.tdk.com/en/2796388/products/product-catalog/ferrites-and-accessories/cores-with-distributed-air-gaps" target="_blank" rel="noopener noreferrer">providing a more standardized solution for it</a>. A standardized distributed air gap core portfolio offer reduces the dependence on custom-built offerings and will revolutionize the Distributed air gap core industry. Such standardized solutions will speed up time to market, improve the return on investment, and stimulate innovation while still allowing for application-specific customization.<iframe width="640" height="360" src="https://www.youtube.com/embed/5KzHH0Mh3bY?&amp;ab_channel=TDK&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true">&nbsp;</iframe>TDK solutions also offer one of the best total costs of ownership and cost/performance ratios due to reliability and reduced proximity losses. This becomes apparent when a single large air gap core is compared to various configurations of distributed air gaps.&nbsp;In fact, the best cost/performance ratio is achieved with three identical air gaps. Thermal image test comparisons between a single large air gap, when contrasted against three identical distributed air gap core sets, clearly show significantly lower outputs at varied frequencies.&nbsp;<h2 dir="ltr">The TDK Standardization Revolution</h2>As a pioneer in magnetics, TDK is the first to offer standard distributed air gap core solutions. TDK continues to invest in the future of magnetics and to support this introduction. The company has invested heavily in new automated manufacturing processes and equipment that improve the quality of air-gapped core products.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzIzNTQ4MzQwMzcwLWltYWdlNC5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 100%px;" class="fr-fic fr-dib" alt="distribute-air-gap-ferrite-core-by-TDK-corporation">A standard ferrite core with distributed air gaps by TDK. Image Source: TDK Corporation[1]TDK is revolutionizing how the market will specify, purchase, and adopt distributed air gapped cores with new standardized products. TDK offers the market easy access to a once highly customized solution. Manufacturing innovation is allowing the industry to take advantage of the technology in new ways. Additional innovation will follow, just as it does whenever a custom solution is standardized.Visit the <a href="https://www.tdk-electronics.tdk.com/en/2796388/products/product-catalog/ferrites-and-accessories/cores-with-distributed-air-gaps" target="_blank" rel="noopener noreferrer">TDK Corporation’s official website</a> to learn more about their innovative distributed air gap cores and how they can enhance the efficiency and performance of your high-power high-frequency applications.<h2 dir="ltr">Conclusion</h2>The development and standardization of distributed air gap cores mark a major advancement in magnetic materials used in electrical/electronics applications. By addressing the challenges with traditional designs, distributed air gap cores enhance efficiency, thermal management, and power handling.&nbsp;TDK’s leadership in standardizing these cores has reduced costs and accelerated innovation across various industries. As applications in industries, renewable energy, automotive, and high-frequency electronics grow, the importance of distributed air gap cores will continue to rise.<h2 dir="ltr">References</h2>[1] TDK Electronics. Distributed air gaps in ferrite cores. Available from: <a href="https://www.tdk-electronics.tdk.com/en/180386/tech-library/publications/ferrites/ferrites-air-gaps-standard/2905788" target="_blank">https://www.tdk-electronics.tdk.com/en/180386/tech-library/publications/ferrites/ferrites-air-gaps-standard/2905788</a>

Distributed air gap cores, traditionally expensive, enhance efficiency and thermal management in high-power, high-frequency applications by reducing reluctance and fringing flux. Standardization efforts by brands like TDK are making these advanced solutions more accessible and cost-effective.

Distributed Air Gap Cores: Standardizing High-Performance Magnetic Solutions

As our world increasingly relies on sound, the demand for pristine audio quality rises. Clear and crisp audio has become essential for various applications such as voice assistants and video conferencing. However, background noise can often interfere with audio quality, making communication and entertainment less enjoyable. Effective audio denoising is essential for these applications.&nbsp;BrainChip has developed an Audio Denoising solution that uses <a href="https://brainchip.com/tenns-akida-2-0/" target="_blank">Temporal Event Neural Networks (TENNs)</a>, a set of algorithms designed to enhance performance and power efficiency.&nbsp;The Audio Denoising solution stands out by minimizing the need for extensive data conversion that consumes significant power. This efficiency supports better audio quality and aligns with the growing demand for sustainable technology solutions in the audio industry.<h2 dir="ltr">The Challenge of Audio Denoising</h2>Audio denoising has long been a complex challenge in signal processing. Traditional methods often lack balancing noise reduction while preserving sound quality.&nbsp;<ul><li dir="ltr">One of the main challenges in audio denoising is distinguishing between desired audio and unwanted noise. This can be especially difficult when the noise has similar characteristics to the audio signal, such as in background conversations or environmental sounds.&nbsp;</li><li dir="ltr">Another challenge is in the computational complexity of audio denoising algorithms. Many traditional methods require significant processing power and memory resources, making them unsuitable for deployment on resource-constrained devices.&nbsp;</li></ul>These limitations have become problematic as the demand for real-time audio processing grows in various applications.&nbsp;<h2 dir="ltr">BrainChip Audio Denoising: A Solution to Unwanted Noise</h2>BrainChip's Audio Denoising solution enhances audio quality by removing unwanted noise. Powered by TENNs, this solution offers improvements in performance and energy efficiency. This makes it a good option for edge-computing audio applications.<h3 dir="ltr">Key Features of BrainChip Audio Denoising</h3><ul><li dir="ltr">The solution includes medium-sized models that achieve an impressive Perceptual Evaluation of Speech Quality (PESQ) score of 3.36 with just 590,000 parameters. This makes it highly scalable to meet specific customer needs.</li><li dir="ltr">The TENN models require fewer computational resources compared to traditional models. They use 12 times fewer multiply-accumulate operations (MACs) and nearly three times fewer parameters. This leads to lower power consumption.</li><li dir="ltr">The Audio Denoising solution comes with integrated Hardware IP. It can easily be incorporated into System-on-Chip (SoC) designs for optimal performance in audio processing tasks.</li><li dir="ltr">It is suitable for a range of devices, including wireless earbuds, VoIP systems, and smart home products. It can also be used as a standalone feature or as part of a more extensive system.</li><li dir="ltr">Customers with a TENNs license can fine-tune the TENN models to cater to specific audio environments. This ensures tailored performance for various use cases.</li></ul><h2 dir="ltr">Benefits of Audio Denoising Solution</h2>BrainChip Audio Denoising solution provides several important benefits for various applications:<ul><li dir="ltr">Improved Sound Quality: This solution enhances audio clarity by removing unwanted noise. This makes conversations, music, and other sounds more enjoyable.</li><li dir="ltr">Better Communication: Denoising improves speech intelligibility in noisy environments, such as busy offices or public places, making conversations easier to understand.</li><li dir="ltr">Enhanced User Experience: Clear audio is important for devices like hearing aids or smart speakers. BrainChip Denoising ensures users can hear content without distractions from background noise.</li><li dir="ltr">Energy Efficiency: The Denoising solution consumes less power. This extends the battery life of devices, which is essential for portable electronics.</li></ul><h2 dir="ltr">Real-World Applications of BrainChip Denoising Solution</h2><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1722510951059-1722510951059.png" width="624" height="416" id="isPasted" class="fr-fic fr-dii">BrainChip Audio Denoising solution can enhance audio quality and improve user experiences in multiple settings.<h3 dir="ltr">1. Smart Home Devices</h3>The solution plays a vital role in smart home technology. By improving the accuracy of voice-controlled systems, it ensures that commands are recognized correctly. This leads to better functionality and user satisfaction.<h3 dir="ltr">2. Industrial Monitoring</h3>In industrial settings, the solution enhances the quality of audio data. This improvement leads to more accurate analysis and predictive maintenance. This offers increased efficiency and safety in operations.<h3 dir="ltr">3. Medical Devices</h3>The technology also applies to medical devices, which aid in vital sign estimation. This capability offers accurate and energy-efficient monitoring of human health, which is important in healthcare settings.<h3 dir="ltr">4. Speech Clarity</h3>The Denoising solution can also be used in voice calls and audio recordings. TENN models remove background noise and allow the speaker's voice to come through clearly. This feature is beneficial for mobile devices and hearing aids, where clear communication is essential.<h3 dir="ltr">5. VoIP Communication</h3>The BrainChip Denoising solution improves audio quality in video conferencing and online meetings. Providing more precise and noise-free audio enhances the experience of remote work. This way, the conversations can be more productive and enjoyable.<h2 dir="ltr">Implementation and Future Directions</h2>BrainChip Audio Denoising solution is also integrated into the Akida 2.0 IP, optimized for TENNs technology. The architecture includes a network of nodes, each equipped with an event-based TENN processing unit. This provides a powerful platform for audio processing tasks.<ul><li dir="ltr">Future developments will aim to improve efficiency by enhancing activation sparsity, which will reduce the number of active computations needed.&nbsp;</li><li dir="ltr">There will also be a focus on exploring more flexible model architectures to adapt to different audio processing needs.&nbsp;</li><li dir="ltr">Additionally, integrated solutions will be developed to combine audio denoising with speech recognition and keyword spotting tasks. This will offer a more comprehensive audio processing toolkit.</li></ul><h2 dir="ltr">Final Words</h2>BrainChip's Audio Denoising Solution is a big step forward in audio processing technology. By managing power consumption and computational demands, the solution supports the development of edge devices that provide clear audio in various environments.&nbsp;This smart technology can make a big difference in how you enjoy your music, videos, and calls. With less noise and better sound, you can focus on what matters.

BrainChip Audio Denoising solution uses Temporal Event Neural Networks (TENNs) to support better audio quality by removing unwanted voices. 

The Audio Revolution at the Edge

In many sectors, sensor networks are composed of thousands of individual sensors, many powered by primary batteries. Certain issues related to limited power autonomy need to be taken into consideration before successfully deploying such networks. Severe conditions can affect battery life, depleting the battery faster and rendering the sensor inoperable, which will in turn affect the quality of measurements and automation.&nbsp;Additionally, every battery which needs to be replaced by personnel involves labour-induced operational costs and generates e-waste, causing ever more environmental harm. According to a recent report by the EU-funded project EnABLES, by 2025 around 78 million batteries(1)&nbsp;powering IoT devices will be dumped globally every day if nothing is done to improve their lifespan.&nbsp;The power bottleneck imposed by batteries needs to be solved in order to benefit from the huge amount of data measured, remote sensor management and smart decision making. Energy harvesting and battery management technology provide a credible solution to improving battery autonomy and are ideally suited for sensing applications. With the development of an innovative Power IC portfolio, Nexperia offers tangible solutions to improve sensors by reaffirming its commitment to empowering engineers in enhancing system efficiency and combating battery waste.<h2>Scenario 1: Energy harvesting enables Plug &amp; Forget sensors</h2>In the dynamic world of intelligent building technology, the integration of cutting-edge sensors plays a pivotal role in enhancing efficiency and sustainability. Designers are increasingly incorporating an array of remote wireless environmental sensors. Enabling uninterrupted sensor operation, energy harvesting technology can successfully drive this innovation. With plug-and-forget IoT sensors, retrofitting or newly equipping buildings becomes easy, ensures long-term sustainability, and reduces maintenance efforts. The energy-harvesting PMICs serve as the backbone of these systems, converting voltage from photovoltaic (PV) cells into rechargeable storage elements, such as lithium-ion batteries.These intelligent sensors boost multifunctionality, encompassing a diverse range of capabilities essential for comprehensive environmental monitoring and control. From temperature and humidity to air quality and motion detection, these sensors offer unparalleled insights into building conditions. The incorporation of PIR motion sensors further enhances security measures, detecting and responding to intrusions with remarkable precision. Also, the integration of CO2 sensors underscores the commitment to occupant health and well-being, facilitating optimal ventilation and air quality management.&nbsp;The synergy between advanced sensors and energy harvesting technology represents a paradigm shift in smart building design. By harnessing the power of innovation, these systems empower building owners and operators to achieve unprecedented levels of efficiency, sustainability, and occupant comfort.<h3>Example of a smart building sensor with unlimited battery life</h3><img width="297" src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzIxMjA0NTc2MzQxLTE3MjEyMDQ1NzYzNDEucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" align="left" alt="A white rectangular device with a light on itDescription automatically generated" class="fr-fil fr-dii">Energy autonomous sensor with PV panel on the top sideNow, in what circumstances can an energy-autonomous smart building sensor be realized? Consider a sensor that is placed in an indoor area, where the light intensity is around 600 lux for 10 hours, a typical situation for office spaces. This sensor will of course consume a certain amount of energy. For this sensor, we could consider the total amount of energy used for measuring and transmitting to be 1 mWh, which is in the ballpark of what we regularly see for these types of applications.&nbsp;With a solar panel area of only 10 cm2 and thanks to Nexperia NEH2000BY’s impressive 80% conversion efficiency, close to 1.1 mWh is converted and used to charge the battery. With this setup, more energy is harvested and converted than is consumed, thereby making the application fully energy autonomous and eliminating the need to replace batteries. &nbsp;&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1721216612401-energy+harvesting.jpg" style="width: 100%px;" class="fr-fic fr-dib">Figure 1: Application block diagram of an energy autonomous building sensor.<h2>Scenario 2: Battery Management IC boosts coin-cell battery life</h2>A large number of wireless building sensors operate at 3V and use such little power that they can run on coin cells. This primary non-rechargeable battery type is cost efficient and easy to buy. Many sensor applications however use pulsed loads which deplete the battery quickly.&nbsp;Nexperia’s <a href="http://www.nexperia.com/category/1670340681253" target="_blank">NBM7100A/B and NBM5100A/B</a> are revolutionary new battery management ICs that can extend the life of a typical Lithium coin cell battery by up to an order of magnitude (10x) while increasing the peak output current available to a pulse load by up to 25x. These ICs contain two high-efficiency DC/DC conversion stages and an intelligent learning algorithm.&nbsp;The first conversion stage transfers energy from the battery to a capacitive storage element at a low rate. The second stage utilizes the stored energy to provide a regulated (programmable from 1.8 V to 3.6 V) high pulse (up to 200 mA) current output. The intelligent learning algorithm monitors the energy used during repetitive load pulse cycles and optimizes first-stage DC-DC conversion to minimize the residual charge in the storage capacitor. When not performing an energy conversion cycle (standby state), these devices consume less than 50 nA.<img width="338" src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzIxMjA0NTc2MzY0LTE3MjEyMDQ1NzYzNjQuanBlZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" align="left" alt="A diagram of a computer componentDescription automatically generated" class="fr-fil fr-dii">Figure 2: Application block diagram of a coin-cell powered Time of Flight IoT sensor&nbsp;&nbsp;<h3>Time-of-Flight sensor with optimized battery performance</h3>Figure 2 depicts an exploration into the application of a time-of-flight sensor, an interesting technology for IoT (Internet of Things) contexts due to its multifaceted utility.&nbsp; Time-of-Flight (ToF) sensors are very versatile; They boost the ability to measure distances to objects, facilitating tasks ranging from open/close detection to presence sensing and fill state determination. However, this versatility comes with a caveat – the substantial power demand necessary to drive the laser diode which is part of the ToF sensor.In this particular case study, a time-of-flight (ToF) module was employed, characterized by a 33ms operational time, a current consumption of 40mA, and a minimum operating voltage requirement of 2.8V.&nbsp;<h3>Battery Life Boosters enhance operational efficiency</h3>Herein lies the challenge: a CR2032 battery, with its source resistance exceeding 10 Ohms, cannot &nbsp;reliably sustain the required 2.8V supply voltage for the TOF module. Even under optimal conditions, the loaded state voltage of the battery dips to 2.6V when fully charged. Consequently, accommodating such power demands necessitates the adoption of larger batteries, such as two AAA cells, inevitably impacting the overall product size.Enter the NBM7100, a game-changer capable of generating a stable 3.0V output voltage, that significantly enhances operational efficiency. With this solution in place, approximately 500,000 measurement cycles become achievable, equivalent to a lifespan of roughly one year when utilizing the sensor once per minute.Moreover, the NBM7100A's VDP output seamlessly supplies continuous voltage to an RF Microcontroller, while the VDH output caters to the ToF sensor's power needs.&nbsp; &nbsp;This innovative approach confers the added advantage of reducing standby current consumption. By selectively powering off the VDH supply during idle phases, leakage current is drastically minimized, plummeting from approximately 10uA to a maximum of 50nA.This exemplary case underscores the pivotal role of battery lifetime boosters in facilitating the creation of more compact product designs, ultimately enhancing usability and user experience.In summary, with energy harvesting enabling Plug &amp; Forget sensors and battery management ICs boosting the coin-cell battery performance, they are both valuable options to power sensors in smart building and many other segments with efficiency and sustainability.<hr>(1)&nbsp; <a href="https://cordis.europa.eu/article/id/430457-up-to-78-million-batteries-will-be-discarded-daily-by-2025-researchers-warn" target="_blank">https://cordis.europa.eu/article/id/430457-up-to-78-million-batteries-will-be-discarded-daily-by-2025-researchers-warn</a>

In many sectors, sensor networks are composed of thousands of individual sensors, many powered by primary batteries. Certain issues related to limited power autonomy need to be taken into consideration before successfully deploying such networks. 

Optimizing battery performance in wireless sensor applications

Much has been discussed about the promise and limitations of large-language models in industries such as education, healthcare and even manufacturing. But what about energy? Could large-language models (LLMs), like those that power ChatGPT, help run and maintain the energy grid?&nbsp;New research, co-authored by&nbsp;<a href="https://seas.harvard.edu/person/na-li" target="_blank">Na Li</a>, Winokur Family Professor of Electrical Engineering and Applied Mathematics at the&nbsp;<a href="https://seas.harvard.edu/" target="_blank">Harvard John A. Paulson School of Engineering and Applied Sciences&nbsp;</a>(SEAS) suggests that LLMs could play an important role in co-managing some aspects of the grid, including emergency and outage response, crew assignments and wildfire preparedness and prevention. But security and safety concerns need to be addressed before LLMs can be deployed in the field.&nbsp;“There is so much hype with large-language models, it’s important for us to ask what LLMs can do well and, perhaps more importantly, what they can’t do well, at least not yet, in the power sector,” said&nbsp;<a href="https://engineering.tamu.edu/electrical/profiles/lxie.html" target="_blank">Le Xie</a>, Professor of Electrical &amp; Computer Engineering at Texas A&amp;M University and corresponding author of the study. &nbsp;“The best way to describe the potential of LLMs in this sector is as a co-pilot. It’s not a pilot yet — but it can provide advice, a second opinion, and very timely responses with very few training data samples, which is really beneficial to human decision making.”The research is published in&nbsp;<a href="https://www.cell.com/joule/abstract/S2542-4351(24)00237-X?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS254243512400237X%3Fshowall%3Dtrue" target="_blank">Joule.</a>&nbsp;The research team, which included engineers from Houston-based energy-provider CenterPoint Energy and grid-operator Midcontinent Independent System Operator, used GPT models to explore the capabilities of LLMs in the energy sector — and identified both strengths and weaknesses.&nbsp;The strengths of LLMs — their ability to generate logical responses from prompts, to learn based on limited data, to delegate tasks to embedded tools and to work with non-text data such as pictures — could be leveraged to perform tasks such as detecting broken equipment, real-time electricity load forecasting, and analyzing wildfire patterns for risk assessments.But there are significant challenges to implementing LLMs in the energy sector — not the least of which is the lack of grid-specific data to train the models. For obvious security reasons, crucial data about the U.S. power system is not publicly available and cannot be made public. Another issue is the lack of safety guardrails. The power grid, like autonomous vehicles, needs to prioritize safety and incorporate large safety margin when making real-time decisions. LLMs also need to get better about providing reliable solutions and transparency around their uncertainties, said Li.&nbsp;“We want foundational LLMs to be able to say ‘I don’t know’ or ‘I only have 50% certainty about this response’, rather than give us an answer that might be wrong,” said Li. “We need to be able to count on these models to provide us with reliable solutions that meet specified standards for safety and resiliency.”All of these challenges give engineers a roadmap for future work.&nbsp;“As engineers, we want to highlight these limitations because we want to see how we can improve them,” said Li. “Power system engineers can help improve security and safety guarantees by either fine tuning the foundational LLM or developing our own foundational model for the power systems. One exciting part of this research is that it is a snapshot in time. Next year or even sooner, we can go back and revisit all these challenges and see if there has been any improvement.”&nbsp;This research was co-authored by Subir Majumder, Lin Dong, Fatemeh Doudi, Yuting Cai, Chao Tian, Dileep Kalathil of Texas A&amp;M University; Kevin Ding of CenterPoint Energy; and Anupam A. Thatte of Midcontinent Independent System Operator.

The promise and limitations of large-language models in the energy sector

Bringing GPT to the grid

Visualizing the energy consumption profile of your embedded IoT device marks the initial step toward achieving low-power design and extended battery life. The subsequent step is analysis, where you can make a significant impact on your energy optimization efforts.See what drains the energy of your IoT devicesOne of the most efficient ways of understanding what actually drain the energy is to connect the power measurements with the actual behaviour of the device. Regardless if your are iterating hardware, firmware or connectivity and application settings, you most likely have prepared your device to output a lot of data and debug reports. If your have done this on UART you are in for treat. <a href="https://www.qoitech.com/otii-arc-pro/" target="_blank">Otii Arc Pro</a> and <a href="https://www.qoitech.com/otii-ace/" target="_blank">Otii Ace Pro</a> from Qoitech come with a clever feature: UART log output sync with power measurements.Otii Arc and Ace Pro’s ability to synchronize expansion port and power measurements while capturing and analyzing UART data offers a powerful solution for debugging and optimizing power consumption during the development and testing phases.UART log output sync with Otii ProAn exceptional feature of Otii Arc/Ace Pro is its ability to correlate UART messages with the power consumption graph. When you mark a specific area on the current graph, all the UART messages that occurred within that time-frame are highlighted in the UART log. Additionally, you can also reverse this process by marking one or multiple messages in the UART log, which will then highlight the corresponding time-frame on the current graph. This feature provides you with invaluable insights into how your device’s code execution affects its power consumption.If you need to save or share the UART records for further analysis or documentation, Otii Arc Pro makes it easy. Simply select one or multiple messages, and you can copy them using the standard keyboard shortcut.UART impact on power consumptionTo maximize the efficiency of this setup, remember to disable UART messages in your device’s firmware when it’s ready for production. This prevents unnecessary energy consumption and ensures that your device operates at its most power-efficient state. <a href="https://www.qoitech.com/blog/uart-energy-drain-iot/" target="_blank">Here</a> you can read more tips and tricks working with UART and insight on what really happens when you do forget to turn it off.How to use UART log syncBefore you can start synchronizing expansion port and power measurements, you need to connect your embedded device to Otii Arc/Ace Pro. Follow these steps to ensure a seamless setup:Step 1: Connect your device to the mains of Otii hardware.This step provides the necessary power supply to your embedded device, ensuring that it operates as intended during testing.Step 2: Connect the UART output from your MCU on the device under test to the Otii hardware expansion port RX. If your device can send commands through UART, also connect TX. For Otii Ace Pro, remember to connect DGND to the main ground, as the main channel and expansion port are isolated.This connection allows Otii Arc/Ace Pro to capture UART data from your device, including debug information and timestamps.<img alt="En bild som visar diagram, skiss, text, rita Automatiskt genererad beskrivning" src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1718673562453-1718673562453.png" width="605" height="454" class="fr-fic fr-dii">Connecting device under test to Otii hardwareStep 3: Configure the Digital voltage level for your device in the General Settings within the Otii application. Enable the UART channel and choose the correct Baud rate for your device.Configuring these settings correctly ensures that Otii Arc/Ace Pro can accurately capture and interpret UART data from your device.Step 4: Analyze the UART prints.Once you’ve completed the setup, you can start analyzing the UART data. The UART prints will be displayed in real-time within the UART log window. You can choose to view the data with relative time, wall time or no time.<img alt="En bild som visar skärmbild, Multimedieprogram, Grafikprogramvara, programvara Automatiskt genererad beskrivning" src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1718673562706-1718673562706.png" width="605" height="431" class="fr-fic fr-dii">UART log are synced with power measurements collected with Otii hardware.Step 5: Copy the UART records.If you need to save or share the UART records for further analysis or documentation, simply select one or multiple messages, and you can copy them using the standard keyboard shortcut (Ctrl-C/Cmd-C) or by right-clicking and selecting “Copy log.”In conclusion, <a href="https://www.qoitech.com/otii-arc-pro/" target="_blank">Otii Arc</a>/<a href="https://www.qoitech.com/otii-ace/" target="_blank">Ace Pro</a> offers sync between the expansion port and power measurements while capturing and analyzing UART data, providing an efficient solution for debugging power consumption during the development and testing phases. Understanding what drains the energy gives insights in how your IoT device operates enabling informed decisions to create low-power electronic devices.See <a href="https://www.qoitech.com/docs/quick-start/setup-uart-recording" target="_blank">documentation</a>.

Visualizing the energy consumption profile of your embedded IoT device marks the initial step toward achieving low-power design and extended battery life. The subsequent step is analysis, where you can make a significant impact on your energy optimization efforts.

Leveraging debug UART logs for power profiling

Authored by Dr. Tony Lewis, CTO at BrainChip, Olivier Coenen, Senior Research Scientist at BrainChip and Yan Ru Pei, research Scientist at BrainChip<h2>The Rise of TENN: A Game Changer in AI</h2>TENN, or Temporal Event-based Neural Network, is redefining the landscape of artificial intelligence by offering a highly efficient alternative to traditional transformer models. Developed by<a href="https://brainchip.com/" target="_blank" rel="noopener noreferrer">&nbsp;BrainChip</a>, this technology aims to address the substantial energy and computational demands of existing AI systems. TENN specializes in sequential or continuous data streams for edge and event-based processing, making it ideal for smart home, automotive, healthcare, and industrial applications.By integrating the concepts of state space models, entirely developed in parallel, independently, and from a generalization of convolution kernels, TENN can handle various transformer tasks such as processing language, time series, and spatiotemporal data. This capability not only broadens its applicability but also enhances its efficiency, offering a reduction in energy consumption by orders of magnitude compared to traditional models.<h2>The Evolution of Kernel Representation</h2><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzE3MjIyNDE3NzczLTE3MTcyMjI0MTc3NzMucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" class="fr-fic fr-dib">Figure 1. A) Receptive field of a simple cell in V1 (DeAngelis, et al, 1995), B) A Gabor Model of the receptive field of simple cell. C) Learned receptive fields of a CNN (Brachmann &amp; Redies,2016)The path from biological neurons to artificial neural networks has witnessed several innovations, with each step bringing us closer to more efficient and capable AI systems. Initially, fixed, non-learnable kernels like the Gabor filter above mimicked the visual processing of the human brain Figure 1A, focusing on specific features within visual stimuli Figure 1B. These early models laid the groundwork for more complex representations.With the advent of deep learning, learnable kernels became prevalent, allowing AI models to adapt their filters based on the data they processed (Figure 1C). However, this flexibility comes with increased computational demands and a surge in the number of parameters. TENN addresses these challenges by revisiting the foundation of kernel representation. It uses a structured approach to reduce parameter explosion and discretization issues, thereby maintaining computational efficiency while retaining flexibility.<h2>Leveraging Polynomials for Advanced Data Representation</h2>TENN utilizes a novel approach by employing orthogonal polynomials, such as Chebyshev and Legendre polynomials, to represent the convolution kernel. This mathematical technique allows TENN to achieve exponential convergence rates in approximating most kernels, even those characterized by discontinuities or complex behaviors. The choice of polynomials ensures that the network can dial in the desired accuracy while maintaining efficiency, even in challenging conditions. It also permits changing discretization, binning, input resolution and frame rate without the need to retrain.The integration of these polynomials into TENN’s architecture allows to transform the network kernel convolutional computations from a buffer mode to a recurrent mode without loss of accuracy or retraining. The buffer mode facilitates efficient training on parallel hardware whereas the recurrent mode permits smaller memory footprint for data processing at the edge. This duality in processing modes ensures stable training and fast execution, which are crucial for real-time AI applications.<h2>TENN’s Dual Modes: Enhancing Flexibility and Efficiency</h2>One of the standout features of TENN is its dual-mode operation, which includes kernel convolutional computations in buffer and recurrent modes. This flexibility allows TENN to adapt to different computational tasks efficiently. In buffer mode, TENN behaves like traditional convolutional neural networks, processing data through various layers of kernel convolutions. In recurrent mode, it leverages the recurrence relationships of classical orthogonal polynomials to process data sequences more naturally and efficiently with much smaller memory requirements.This dual capability not only makes TENN versatile but also enhances its performance across a wide range of applications. From real-time language translation to complex data analysis in autonomous vehicles, TENN’s dual modes enable it to deliver high performance during training while minimizing energy usage for inference at the edge.<h2>Implementation and Future Directions</h2>The implementation of TENN within BrainChip’s hardware in the Akida 2.0, showcases a significant step forward in hardware-accelerated AI. Akida 2.0’s architecture is designed to fully exploit TENN’s capabilities, featuring a mesh network of nodes each equipped with an event-based TENN processing unit. This design ensures scalability and enhances computational efficiency, making it suitable for deployment in environments where power and space are limited.Looking ahead, BrainChip plans to continue refining TENN’s capabilities, focusing on enhancing activation sparsity and exploiting more of the polynomial space. These advancements are expected to further improve the efficiency and applicability of TENN in more complex AI tasks.<h2>Broad Applications and Impressive Performance</h2>TENN’s application range is impressive, extending from audio processing tasks like noise reduction and speech recognition to more demanding applications such as biomedical signal processing and industrial monitoring. Its ability to efficiently process large volumes of data in real-time makes it a potent tool for developing advanced AI applications in various sectors.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzE3MjIyNDU2Mjk4LWltYWdlLTIucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Figure 2. Comparison of TENNs on an NLP task sentence generation. The score we use is related to perplexity but transformed to so that the higher the score the better.Performance metrics indicate that TENN not only holds its own against larger models like GPT-2 but often exceeds them in efficiency and speed, particularly in tasks that benefit from its event-based processing capabilities. TENN produced text at the rate of 2500 words/min vs 10 words/min on the same laptop CPU, single threaded performance.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzE3MjIyNDY4OTQ2LVNjcmVlbnNob3QtMjAyNC0wNS0xNS1hdC0zLjU0LjI04oCvUE0ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Figure 3. Audio denoised on standard benchmark. TENN processes audio directly, saving a power consuming data conversion step.On speech enhancement through denoising, TENN was compared to the state of the art (SoTA) networks. While providing almost the same performance with this particular implementation, the number of parameters and number of operations (MACs) with TENN are nearly 3 times and 12 times less.TENN is a spatiotemporal network that performs as well with visual inputs in either image sequence format or event-based visual data. Below is an example of gesture recognition on the DVS Hand Gesture Recognition dataset (IBM DVS128).<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1717222487490-image001.gif" style="width: 100%px;" class="fr-fic fr-dib"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzE3MjIyNDkyNzQyLWltYWdlLTctZTE3MTU3MTQzNjAxNjUucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">These results highlight TENN’s potential to reshape the future of AI, making it more accessible and sustainable.Conclusion: A New Era of Efficient AI<a href="http://www.brainchip.com/" target="_blank">BrainChip’s</a> development of TENN marks a significant milestone in the evolution of artificial intelligence. By addressing key challenges related to power consumption and computational efficiency, TENN is poised to lead a new era of AI technology. As it continues to evolve, its impact is expected to grow, opening new possibilities for smart technology across global industries.

TENN, developed by BrainChip, is an efficient AI technology designed for edge and event-based processing, reducing energy and computational demands for applications in various sectors.

Introducing TENN: Revolutionizing Computing with an Energy Efficient Transformer Replacement

In the blink of an eye, the unruly, superheated plasma that drives a fusion reaction can lose its stability and escape the strong magnetic fields confining it within the donut-shaped fusion reactor. These getaways frequently spell the end of the reaction, posing a core challenge to developing fusion as a non-polluting, virtually limitless energy source.But a Princeton-led team composed of engineers, physicists, and data scientists from the University and the U.S. Department of Energy’s <a href="https://www.pppl.gov/" target="_blank">Princeton Plasma Physics La</a><a href="https://www.pppl.gov/" target="_blank">boratory</a> (PPPL) have harnessed the power of artificial intelligence to predict — and then avoid — the formation of a specific plasma problem in real time.In experiments at the <a href="https://www.ga.com/magnetic-fusion/diii-d" target="_blank">DIII-D National Fusion Facility</a> in San Diego, the researchers demonstrated their model, trained only on past experimental data, could forecast potential plasma instabilities known as tearing mode instabilities up to 300 milliseconds in advance. While that leaves no more than enough time for a slow blink in humans, it was plenty of time for the AI controller to change certain operating parameters to avoid what would have developed into a tear within the plasma’s magnetic field lines, upsetting its equilibrium and opening the door for a reaction-ending escape.“By learning from past experiments, rather than incorporating information from physics-based models, the AI could develop a final control policy that supported a stable, high-powered plasma regime in real time, at a real reactor,” said research leader <a href="https://engineering.princeton.edu/faculty/egemen-kolemen" target="_blank">Egemen Kolemen</a>, associate professor of <a href="https://mae.princeton.edu/" target="_blank">mechanical and aerospace engineering</a> and the <a href="https://acee.princeton.edu/" target="_blank">Andlinger Center for Energy and the Environment</a>, as well as staff research physicist at PPPL.The research opens the door for more dynamic control of a fusion reaction than current approaches, and it provides a foundation for using artificial intelligence to solve a broad range of plasma instabilities, which have long been obstacles to achieving a sustained fusion reaction. The team published <a href="https://www.nature.com/articles/s41586-024-07024-9" target="_blank">their findings</a> in Nature on February 21.“Previous studies have generally focused on either suppressing or mitigating the effects of these tearing instabilities after they occur in the plasma,” said first author <a href="https://physics.cau.ac.kr/BM/bm_1_view.php?idx=76" target="_blank">Jaemin Seo</a>, an assistant professor of physics at Chung-Ang University in South Korea who performed much of the work while a postdoctoral researcher in Kolemen’s group. “But our approach allows us to predict and avoid those instabilities before they ever appear.”<h2>Superheated plasma swirling in a donut-shaped device</h2>Fusion takes place when two atoms — usually light atoms like hydrogen — come together to form one heavier atom, releasing a large amount of energy in the process. The process powers the Sun, and, by extension, makes life on Earth possible.However, getting the two atoms to fuse is tricky, as it takes massive amounts of pressure and energy for the two atoms to overcome their mutual repulsion.Fortunately for the Sun, its massive gravitational pull and extremely high pressures at its core allow fusion reactions to proceed. To replicate a similar process on the Earth, scientists instead use extremely hot plasma and extremely strong magnets.In donut-shaped devices known as tokamaks — sometimes referred to as “stars in jars” — magnetic fields struggle to contain plasmas that reach above 100 million degrees Celsius, hotter than the center of the Sun.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1709522056008-magnetic_tearing_v3_simplified-copy.webp" style="width: 100%px;" class="fr-fic fr-dib">The researchers harnessed the power of AI to predict and avoid the formation of a tearing instability (left), which could quickly result in plasma disruption and the termination of the fusion reaction. (Courtesy of the researchers).While there are many types of plasma instabilities that can terminate the reaction, the Princeton team concentrated on solving tearing mode instabilities, a disturbance in which the magnetic field lines within a plasma actually break and create an opportunity for the plasma’s subsequent escape.“Tearing mode instabilities are one of the major causes of plasma disruption, and they will become even more prominent as we try to run fusion reactions at the high powers required to produce enough energy,” said Seo. “They are an important challenge for us to solve.”<h2>Fusing artificial intelligence and plasma physics</h2>Since tearing mode instabilities can form and derail a fusion reaction in milliseconds, the researchers turned to artificial intelligence for its ability to quickly process and act in response to new data.But the process to develop an effective AI controller was not as simple as trying out a few things on a tokamak, where time is limited, and the stakes are high.Co-author&nbsp;<a href="https://mae.princeton.edu/people/research-staff/jalalvand" target="_blank">Azarakhsh Jalalvand</a>, a research scholar in Kolemen’s group, compared teaching an algorithm to run a fusion reaction in a tokamak to teaching someone how to fly a plane.“You wouldn’t teach someone by handing them a set of keys and telling them to try their best,” Jalalvand said. “Instead, you’d have them practice on a very intricate flight simulator until they’ve learned enough to try out the real thing.”Like developing a flight simulator, the Princeton team used data from past experiments at the DIII-D tokamak to construct a deep neural network capable of predicting the likelihood of a future tearing instability based on real-time plasma characteristics.They used that neural network to train a reinforcement learning algorithm. Like a pilot trainee, the reinforcement learning algorithm could try out different strategies for controlling plasma, learning through trial and error which strategies worked and which did not within the safety of a simulated environment.“We don’t teach the reinforcement learning model all of the complex physics of a fusion reaction,” Jalalvand said. “We tell it what the goal is — to maintain a high-powered reaction — what to avoid — a tearing mode instability — and the knobs it can turn to achieve those outcomes. Over time, it learns the optimal pathway for achieving the goal of high power while avoiding the punishment of an instability.”While the model went through countless simulated fusion experiments, trying to find ways to maintain high power levels while avoiding instabilities, co-author&nbsp;<a href="https://www.pppl.gov/people/sangkyeun-kim" target="_blank">SangKyeun Kim</a> could observe and refine its actions.“In the background, we can see the intentions of the model,” said Kim, a staff research scientist at PPPL and former postdoctoral researcher in Kolemen’s group. “Some of the changes that the model wants are too rapid, so we work to smooth and calm the model. As humans, we arbitrate between what the AI wants to do and what the tokamak can accommodate.”Once they were confident in the AI controller’s abilities, they tested it during an actual fusion experiment at the D-III D tokamak, observing as the controller made real-time changes to certain tokamak parameters to avoid the onset of an instability. These parameters included changing the shape of the plasma and the strength of the beams inputting power into the reaction.“Being able to predict instabilities ahead of time can make it easier to run these reactions than current approaches, which are more passive,” said Kim. “We no longer have to wait for the instabilities to occur and then take quick corrective action before the plasma becomes disrupted.”<h2>Powering into the future</h2>While the researchers said the work is a promising proof-of-concept demonstrating how artificial intelligence can effectively control fusion reactions, it is only one of many next steps already ongoing in Kolemen’s group to advance the field of fusion research.The first step is to get more evidence of the AI controller in action at the DIII-D tokamak, and then expand the controller to function at other tokamaks.“We have strong evidence that the controller works quite well at DIII-D, but we need more data to show that it can work in a number of different situations,” said first author Seo. “We want to work toward something more universal.”A second line of research involves expanding the algorithm to handle many different control problems at the same time. While the current model uses a limited number of diagnostics to avoid one specific type of instability, the researchers could provide data on other types of instabilities and give access to more knobs for the AI controller to tune.“You could imagine one large reward function that turns many different knobs to simultaneously control for several types of instabilities,” said co-author&nbsp;<a href="https://mae.princeton.edu/people/graduate-students/shousha" target="_blank">Ricardo Shousha</a>, a postdoc at PPPL and former graduate student in Kolemen’s group who provided support for the experiments at DIII-D.And on the route to developing better AI controllers for fusion reactions, researchers might also gain more understanding of the underlying physics. By studying the AI controller’s decisions as it attempts to contain the plasma, which can be radically different than what traditional approaches might prescribe, artificial intelligence may be not only a tool to control fusion reactions but also a teaching resource.“Eventually, it may be more than just a one-way interaction of scientists developing and deploying these AI models,” said Kolemen. “By studying them in more detail, they may have certain things that they can teach us too.”<hr>The paper, “<a href="https://www.nature.com/articles/s41586-024-07024-9" target="_blank">Avoiding fusion plasma tearing instability with deep reinforcement learning</a>,” was published February 21 in Nature. In addition to Kolemen, Seo, Jalalvand, Kim, and Shousha, co-authors include Rory Conlin, Andrew Rothstein, Joseph Abbate and Josiah Wai of Princeton University, as well as Keith Erickson of PPPL.The work was supported by the U.S. Department of Energy’s Office of Fusion Energy Sciences, as well as the National Research Foundation of Korea (NRF). The authors also acknowledge the use of the DIII-D National Fusion Facility, a Department of Energy Office of Science user facility.

In the blink of an eye, the unruly, superheated plasma that drives a fusion reaction can lose its stability and escape the strong magnetic fields confining it within the donut-shaped fusion reactor. 

Engineers use AI to wrangle fusion power for the grid

Energy is everywhere, affecting everything, all the time. And it can be manipulated and converted into the kind of energy that we depend on as a civilization. But transforming this ambient energy (the result of gyrating atoms and molecules) into something we can plug into and use when we need it requires specific materials.These energy materials — some natural, some manufactured, some a combination — facilitate the conversion or transmission of energy. They also play an essential role in how we store energy, how we reduce power consumption, and how we develop cleaner, efficient energy solutions.“Advanced materials and clean energy technologies are tightly connected, and at Georgia Tech we’ve been making major investments in people and facilities in batteries, solar energy, and hydrogen, for several decades,” said&nbsp;<a href="https://ae.gatech.edu/directory/person/timothy-charles-lieuwen" target="_blank">Tim Lieuwen</a>, the David S. Lewis Jr. Chair and professor of aerospace engineering, and executive director of Georgia Tech’s Strategic Energy Institute (<a href="https://research.gatech.edu/energy" target="_blank">SEI</a>).That research synergy is the underpinning of&nbsp;<a href="https://research.gatech.edu/energymaterials" target="_blank">Georgia Tech Energy Materials Day (March 27)</a>, a gathering of people from academia, government, and industry, co-hosted by SEI, the Institute for Materials (<a href="https://research.gatech.edu/materials" target="_blank">IMat</a>), and the Georgia Tech Advanced Battery Center. This event aims to build on the momentum created by&nbsp;<a href="https://research.gatech.edu/georgia-tech-battery-day-reveals-opportunities-energy-storage-research" target="_blank">Georgia Tech Battery Day</a>, held in March 2023, which drew more than 230 energy researchers and industry representatives.“We thought it would be a good idea to expand on the Battery Day idea and showcase a wide range of research and expertise in other areas, such as solar energy and clean fuels, in addition to what we’re doing in batteries and energy storage,” said&nbsp;<a href="https://www.mse.gatech.edu/people/matthew-mcdowell" target="_blank">Matt McDowell</a>, associate professor in the George W.&nbsp;<a href="https://www.me.gatech.edu/" target="_blank">Woodruff School of Mechanical Engineering</a> and the&nbsp;<a href="https://www.mse.gatech.edu/" target="_blank">School of Materials Science and Engineering (MSE)</a>, and co-director, with&nbsp;<a href="https://www.mse.gatech.edu/people/gleb-yushin" target="_blank">Gleb Yushin</a>, of the Advanced Battery Center.Energy Materials Day will bring together experts from academia, government, and industry to discuss and accelerate research in three key areas: battery materials and technologies, photovoltaics and the grid, and materials for carbon-neutral fuel production, “all of which are crucial for driving the clean energy transition,” noted&nbsp;<a href="https://www.mse.gatech.edu/people/eric-vogel" target="_blank">Eric Vogel</a>, executive director of IMat and the Hightower Professor of Materials Science and Engineering.“Georgia Tech is leading the charge in research in these three areas,” he said. “And we’re excited to unite so many experts to spark the important discussions that will help us advance our nation’s path to net-zero emissions.”<h2>Building an Energy Hub</h2>Energy Materials Day is part of an ongoing, long-range effort to position Georgia Tech, and Georgia, as a go-to location for modern energy companies. So far, the message seems to be landing. Georgia has had more than $28 billion invested or announced in electric vehicle-related projects since 2020. And Georgia Tech was recently ranked by U.S. News &amp; World Report as the&nbsp;<a href="https://research.gatech.edu/georgia-tech-named-top-ranked-public-university-energy" target="_blank">top public university for energy research</a>.Georgia has become a major player in solar energy, also, with the announcement last year of a $2.5 billion plant being developed by Korean solar company Hanwha Qcells, taking advantage of President Biden’s climate policies. Qcells’ global chief technology officer, Danielle Merfeld, a member of SEI’s External Advisory Board, will be the keynote speaker for Energy Materials Day.“Growing these industry relationships, building trust through collaborations with industry — these have been strong motivations in our efforts to create a hub here in Atlanta,” said Yushin, professor in MSE and co-founder of Sila Nanotechnologies, a battery materials startup valued at more than $3 billion.McDowell and Yushin are leading the battery initiative for Energy Materials Day and they’ll be among 12 experts making presentations on battery materials and technologies, including six from Georgia Tech and four from industry. In addition to the formal sessions and presentations, there will also be an opportunity for networking.“I think Georgia Tech has a responsibility to help grow a manufacturing ecosystem,” McDowell said. “We have the research and educational experience and expertise that companies need, and we’re working to coordinate our efforts with industry.”<a href="https://research.gatech.edu/marta-hatzell" target="_blank">Marta Hatzell</a>, associate professor of mechanical engineering and chemical and biomolecular engineering, is leading the carbon-neutral fuel production portion of the event, while&nbsp;<a href="https://research.gatech.edu/juan-pablo-correa-baena" target="_blank">Juan-Pablo Correa-Baena</a>, assistant professor in MSE, is leading the photovoltaics initiative.They’ll be joined by a host of experts from Georgia Tech and institutes across the country, “some of the top thought leaders in their fields,” said Correa-Baena, whose lab has spent years optimizing a semiconductor material for solar energy conversion.“Over the past decade, we have been working to achieve high efficiencies in solar panels based on a new, low-cost material called halide perovskites,” he said. His lab recently discovered how to&nbsp;<a href="https://coe.gatech.edu/news/2023/12/researchers-find-they-can-stop-degradation-promising-solar-cell-materials" target="_blank">prevent the chemical interactions that can degrade it</a>. “It’s kind of a miracle material, and we want to increase its lifespan, make it more robust and commercially relevant.”While Correa-Baena is working to revolutionize solar energy, Hatzell’s lab is designing materials to clean up the manufacturing of clean fuels.“We’re interested in decarbonizing the industrial sector, through the production of carbon-neutral fuels,” said Hatzell, whose lab is designing new materials to make clean ammonia and hydrogen, both of which have the potential to play a major role in a carbon-free fuel system, without using fossil fuels as the feedstock. “We’re also working on a collaborative project focusing on assessing the economics of clean ammonia on a larger, global scale.”The hope for Energy Materials Day is that other collaborations will be fostered as industry’s needs and the research enterprise collide in one place — Georgia Tech’s Exhibition Hall — over one day. The event is part of what Yushin called “the snowball effect.”“You attract a new company to the region, and then another,” he said. “If we want to boost domestic production and supply chains, we must roll like a snowball gathering momentum. Education is a significant part of that effect. To build this new technology and new facilities for a new industry, you need trained, talented engineers. And we’ve got plenty of those. Georgia Tech can become the single point of contact, helping companies solve the technical challenges in a new age of clean energy.”

Energy materials facilitate the conversion or transmission of energy. They also play an essential role in how we store energy, reduce power consumption, and develop cleaner, efficient energy solutions.

Energy Materials: Driving the Clean Energy Transition

<section id="isPasted">The ubiquity of electronics in today’s world permeates every facet of our lives, spanning our homes and workplaces. With our increasing dependence on electronic devices, it is imperative that these systems seamlessly operate within their electromagnetic environments. Addressing Electromagnetic Interference (EMI) and fostering Electromagnetic Compatibility (EMC) emerge as critical concerns. This blog post aims to explore effective strategies for shielding PCBA designs against electromagnetic interference.The journey of understanding and addressing EMI and EMC started as far back as the 1940s and 1950s. It was during this period that concerns began to rise about noise from motors traveling along power lines and interfering with sensitive equipment. As electronics have grown more complex and sophisticated, these challenges have only become more intricate and widespread.In simple terms, EMC refers to a system’s ability to operate harmoniously within its electromagnetic environment without causing interference. EMI, in contrast, refers to any form of electromagnetic disturbance that interrupts, obstructs, or otherwise degrades the effective performance of an electronic system.Ensuring optimal EMC in electronic systems requires a dual strategy. The system at the receiving end, or the ‘victim’ system, should be robust enough to withstand higher levels of EMI (known as susceptibility). Concurrently, the system at the source of the interference, or the ‘culprit’ system, should be designed in a way that emits as little EMI as possible (known as emission).In this context, a well-crafted PCBA must not only curtail its EMI emissions but also demonstrate resilience against high EMI levels. This article will primarily focus on the latter aspect—how to fortify your PCBA design against EMI, ensuring its integrity and optimal performance in challenging electromagnetic environments.<h2>Understanding Electromagnetic Interference</h2>PCBA designs that incorporate every available filter and shielding measure blindly in the hope that something will work are not only ineffective but also expensive. An optimal PCBA design is rarely achieved with a haphazard strategy. To reduce, control, or even eliminate EMI emissions, you need a fundamental understanding of their origins.In electronic systems, EMI emissions can interfere with normal operation by creating electromagnetic ‘noise’ or disturbances. EMI emissions originate from a variety of sources.<h2>Sources of EMI</h2>One of the primary causes of EMI emissions is common mode currents. Common-mode currents exist at locations where they were never intended and flow in the same direction through multiple conductors like power supply lines, signal cables, or grounding paths. These unwanted parasitic effects can often be attributed to poor <a href="https://www.macrofab.com/blog/high-speed-routing-design" target="_blank">routing design</a>. To deal with EMI issues effectively, it’s important to be aware of all the different ways these unintentionally induced currents can occur:<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1704810405650-1704810405650.png" alt="Parasitic capacitance" class="fr-fic fr-dii"><ul><li>Parasitic capacitance: Capacitance is the ability to store charge, and parasitic capacitance is the unwanted capacitance that occurs in an electronic circuit because of the close proximity of the circuit components. Parasitic capacitance can form an alternate path for the current that has less impedance (or resistance to the flow of current) than the intended path.We know that when alternating current (AC) flows in an electronic circuit, it generates an alternating magnetic field around it. If another conductive path is nearby (like the unintentional one formed due to parasitic capacitance), this alternating magnetic field can induce an alternating voltage (and hence an alternating current) in that path. This is known as capacitive coupling, or electric field coupling.This unintentionally induced current can interfere with the normal operation of the circuit, creating noise or disturbances — the essence of Electromagnetic Interference.Moreover, these unintentionally formed conductive paths (due to parasitic capacitance) can act as antennae, especially at high frequencies, emitting electromagnetic waves into the surrounding space. This is another way in which EMI can be caused: the emitted electromagnetic waves can cause interference in other nearby electronic devices.In short, parasitic capacitance can enable electric field coupling in a circuit, leading to EMI that can disturb the operation of the circuit itself or other nearby devices.</li><li>Parasitic inductance: Parasitic inductance is the unintentional inductance that exists in a circuit due to the physical layout of the circuit components. This unintentional inductance facilitates the process of magnetic field coupling by providing unintended loops through which magnetic field lines can pass, potentially inducing strong currents in nearby conductors and leading to EMI.As with parasitic capacitance, parasitic inductance effects become more pronounced at higher frequencies, leading to increased EMI potential.</li><li>High-speed fast rise time signals: Switching devices, such as transistors, ICs, and digital logic circuits, generate fast rise and fall times, resulting in high-frequency components in their signals. These high-speed signals can radiate electromagnetic energy, especially if not properly controlled.</li><li>Emissions from intentional signals: Intentional signals, those necessary for the operation of a circuit, can unexpectedly produce EMI. Let’s consider two main sources: loop-mode emissions and common-mode emissions.Loop-mode emissions are generated during the normal operation of your circuit. They arise from the electromagnetic fields created by current that flows through the signal traces of your circuit and then returns either through a reference plane or a specifically designed return path.On the other hand, common-mode emissions occur when the return current flows through a trace positioned near the edge of the reference plane. To reduce the risk of common-mode emissions, one practical design tip is to maintain a safe distance between high-frequency traces and the edge of the PCBA board, ideally at least a half-inch.</li></ul><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1704810405774-1704810405774.png" alt="Emission unintentional signals" class="fr-fic fr-dii"><ul><li>Emissions from unintentional signals: Unintentional signals originate from an intentional signal that has somehow transferred onto a conductor not designed to carry that signal. This unwanted coupling can lead to EMI. For example, common mode currents from these signals can create EMI. Similarly, ‘crosstalk,’ where an intentional signal is transferred onto an unintended conductor, for instance, crosstalk coupling between high-speed signal traces and I/O traces can lead to EMI.</li><li>Heatsinks: A heatsink, which is usually metal, is placed on top of a high-speed integrated circuit to facilitate cooling. The close proximity allows the IC’s internal currents to unintentionally couple with the heatsink, causing it to act as an antenna. Since these heatsinks are mounted very close to the IC’s high-frequency part, internal currents can easily flow through them.To manage these emissions, it’s common practice to ground the heatsink to the ground reference of the PCBA board. This grounding reduces the voltage difference between the heatsink and the ground reference plane, thereby minimizing the associated emissions.</li></ul><h2>Effects of EMI on PCBAs</h2>EMI effects on PCBAs range from minor disturbances in functionality to more severe outcomes, including total system failure. Here’s an overview of potential issues:<ul><li>System Performance Degradation: EMI can subtly impact the functionality of PCBAs, causing difficult-to-diagnose intermittent issues. These may include unexpected system behaviors, erratic errors, or slower-than-normal performance. A system failure may not always occur, but it will make the system feel unreliable and frustrating.</li></ul><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1704810405822-1704810405822.png" alt="Error file not found" class="fr-fic fr-dii"><ul><li>Data Corruption: Electromagnetic disturbances can interfere with the data transmission process, leading to errors in data reading or writing. As a result, data can be corrupted or lost, which has disastrous consequences in systems like medical devices and aerospace electronics.</li><li>Signal Interference: High-frequency EMI, in particular, can interfere with wireless communication signals. If a PCBA is part of a device that relies on wireless communication, EMI can cause dropped signals, decreased signal strength, or increased error rates.</li><li>False triggering: EMI can cause false triggering of sensitive devices. As a result, system functions could be activated unexpectedly.</li><li>Regulatory Compliance Issues: Devices that generate too much EMI can fail to meet regulatory standards, which stipulate limits on the amount of electromagnetic noise a device can emit. In some markets, non-compliance can result in the inability to sell the product.</li></ul><h2>Design Considerations to Shield PCBA from EMI</h2>EMI is usually confined to specific frequencies, not spread widely across the spectrum. Resonant frequencies in your system will often enhance the EMI’s effect, but resonances aren’t the source of EMI. Instead, they act as loudspeakers that amp up existing EMI, making it more noticeable and problematic.The best way to deal with EMI effectively is to know your system inside out, including its unintended (‘parasitic’) elements, potential resonances, and how your circuitry is combined. By taking this into consideration early in the design process, you’ll be able to save yourself a lot of trouble later in the process. As a result, your system will be more likely to meet EMC standards right away.<h3>Inductance Effects</h3>When dealing with high-frequency signals, inductance is a crucial factor to consider. The reason: When current flows through metal, it creates inductance, resulting in an impedance (resistance to current flow) and a voltage potential. Noise can be introduced across grounding planes, signal strength can decrease, and EMI emissions can rise.<h3>Consider Critical Signals</h3>Your attention should be focused on signals that switch frequently since not every signal on a PCBA contributes significantly to EMI emissions. These signals include clock signals, memory signals, address and data bus signals, data strobe signals, video signals, and any other signal with high speed and wide bandwidth. Pay attention to these signals as they could potentially emit EMI.<h3>Current Radiates and Not Voltage</h3>For matters related to EMI, the current is of primary concern. Always remember the fact that current radiates and not voltage. EMI is primarily caused by the flow of electrical current, not voltage alone. The current generates electromagnetic fields, which radiate and interfere with other devices. While voltage drives the current flow it does not directly produce radiation.Managing current flow is vital to reduce EMI by minimizing the generation and propagation of electromagnetic fields. Effective circuit design, shielding, and grounding techniques help control current and minimize interference with other electronic systems. It is therefore useful to analyze the currents on a trace as well as the voltage waveforms.<h3>Explicit Design of Return Paths</h3>In many instances, emission sources are closely related to the return current path or absence of it. Board layout design shouldn’t be merely about routing a trace from the driver to the receiver. Instead, it is equally important to take into account the return current signal path. When you consider these factors during the design phase, you can reduce the likelihood of encountering EMI problems during testing or operation.<h3>Tips to Mitigate or Avoid Electromagnetic Interference</h3>You want to create robust, reliable devices that deliver optimal performance, despite potential interference. Here are seven tips that will help you achieve this goal.<h3>Tip 1: Using Shielding Techniques</h3><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1704810405866-1704810405866.png" alt="Using shield techniques" class="fr-fic fr-dii">The first line of defense against electromagnetic interference is shielding. Use metals that absorb or reflect electromagnetic radiation, such as steel, copper, and aluminum.Shielding methods are available in several forms. The enclosure shielding method involves wrapping your entire device in a conductive casing. The cable shielding method uses shielded cables to shield signals from EMI interference. The last method is board-level shielding, which involves placing small shields around specific components.<h3>Tip 2: Implementing Proper Grounding Strategies</h3>A safe path for dissipating interference is provided by grounding. It can minimize EMI. In order to implement effective grounding, create a single unified ground point to avoid ground loops. Failing to ground properly could lead to EMI accumulation and potentially wreak havoc on your device’s components.<h3>Tip 3: Utilizing Filtering Components</h3><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1704810405905-1704810405905.png" alt="Utilizing filtering components" class="fr-fic fr-dii">A common practice is to use decoupling capacitors between the power plane and the ground plane on a PCB. These capacitors serve two important functions.The first benefit of these capacitors is that they maintain the proper functionality and signal integrity, ensuring stable voltage levels and a smooth power supply to the circuit. Furthermore, decoupling capacitors reduce noise injected into the ground-reference plane and power plane pairs. By taking this action, EMI emissions from the edge of your circuit board are significantly reduced, providing an effective method of mitigating EMI in your electronic device.<h3>Tip 4: Pay Attention to Component Placement and Board Layout</h3>EMI is greatly affected by how components are arranged on a PCBA. Critical components should be placed first, based on their orientation and proximity. Components that generate high EMI should be located away from those that are sensitive.However, placement is just one part of the equation. Thoughtful board routing is another part. By meticulously selecting which layers to route which signals, you can allow return currents to remain near the corresponding signal traces. This careful routing strategy can considerably enhance the EMC performance of your device compared to random routing.Also, remember to consider the return current path in your design. This often-overlooked aspect is crucial in achieving good EMC performance and minimizing EMI.<h3>Tip 5: Be Strategic with Board and Trace Routing</h3>Your trace routing on a PCBA can dramatically affect EMI. Aim for short and direct traces to minimize EMI risks.Parallel traces can act as antennas, while perpendicular traces can reduce crosstalk. Consider multilayer boards, where power and ground planes shield traces.<h3>Tip 6: Opt for Differential Signaling When Possible</h3>Differential signaling is a technique for transmitting information that can reduce EMI quite effectively. It transmits two versions of a signal - one is the inverse of the other.When the receiver compares the difference between these signals, it can accurately reconstruct the original signal. This method is resistant to EMI, as any interference picked up by the transmission line will affect both signals equally, and hence will be canceled out when the difference is taken.<h3>Tip 7: Never Underestimate Regular Testing and Revisions</h3><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1704810406035-1704810406035.png" alt="Regular testing revisions" class="fr-fic fr-dii">EMI tests, like radiated emissions testing and conducted emissions testing, can help pinpoint EMI sources in your design.Schedule these tests throughout the design process, and revise them as necessary to curb EMI. Consistent testing and revisions ensure your design adheres to EMI standards and regulations.<h2>Conclusion</h2>EMI poses a substantial threat to the efficient performance of PCBA designs, affecting system performance and regulatory compliance. Therefore it is crucial to understand the different ways your circuit can produce common-mode currents that cause EMI emissions.Armed with this knowledge, you can make well-informed trade-offs in your design process. Several design considerations can be used to mitigate EMI, such as shielding techniques, proper grounding strategies, the use of filtering components, strategic component placement, and differential signaling.By diligently implementing these strategies during the design phase, it’s possible to mitigate EMI issues, save on redesign costs, ensure regulatory compliance, and guarantee the efficient operation of the electronic system.</section><a href="https://www.macrofab.com/blog.rss" target="_blank">&nbsp;</a><h4>Download MacroFab’s <a href="https://www.macrofab.com/documents/pcba-design-handbook-2023/" target="_blank" rel="noopener noreferrer">Engineering Essentials: The PCBA Design Handbook</a></h4>to learn more about PCBA design basics.

Trace routing on a PCBA can dramatically affect EMI.  Find out what else you need to know about Electromagnetic Interference

Safeguarding Your PCBA Design from Electromagnetic Interference

Render of the Mijolnia product. Image: Mijolnia

In response to the OKDo Engineering Challenge provocation, the CEO of Mijolnia, Promise Okwuchukwu, submitted a proposal to use the ROCK SBC in their Mijolnia Prime 1, a rechargeable battery system designed to store solar charge and provide energy to households and businesses.  

OKdo ROCK Challenge: Interview with Promise Okwuchukwu. Bringing energy storage to Africa

As information and communication technologies (ICT) process data, they convert electricity into heat. Already today, the global ICT ecosystem’s CO2 footprint rivals that of aviation. It turns out, however, that a big part of the energy consumed by computer processors doesn’t go into performing calculations. Instead, the bulk of the energy used to process data is spent shuttling bytes between the memory to the processor.In a <a href="https://dx.doi.org/10.1038/s41928-023-01064-1" target="_blank" rel="noopener">paper published</a> in the journal Nature Electronics, researchers from EPFL’s School of Engineering in the <a href="https://www.epfl.ch/labs/lanes/" target="_blank">Laboratory of Nanoscale Electronics and Structures (LANES)</a> present a new processor that tackles this inefficiency by integrating data processing and storage onto a single device, a so-called in-memory processor. They broke new ground by creating the first in-memory processor based on a two-dimensional semiconductor material to comprise more than 1000 transistors, a key milestone on the path to industrial production.<h2>Von Neuman’s legacy</h2>According to Andras Kis, who led the study, the main culprit behind the inefficiency of today’s CPUs is the universally adopted von Neumann architecture. Specifically, the physical separation of the components used to perform calculations and to store data. Because of this separation, processors need to retrieve data from the memory to perform calculations, which involves moving electrical charges, charging and discharging capacitors, and transmitting currents along lines – all of which dissipate energy.Until around 20 years ago, this architecture made sense, as different types of devices were required for data storage and processing. But the von Neumann architecture is increasingly being challenged by more efficient alternatives. “Today, there are ongoing efforts to merge storage and processing into a more universal in-memory processors that contain elements which work both as a memory and as a transistor,” Kis explains. His lab has been exploring ways to achieve this goal using molybdenum disulfide (MoS2), a semiconductor material.<h2>A new two-dimensional processor architecture</h2>In their Nature Electronics paper, Guilherme Migliato Marega, doctoral assistant at LANES, and his co-authors present an MoS2-based in-memory processor dedicated to one of the fundamental operations in data processing: vector-matrix multiplication. This operation is ubiquitous in digital signal processing and the implementation of artificial intelligence models. Improvements in its efficiency could yield substantial energy savings throughout the entire ICT sector.Their processor combines 1024 elements onto a one-by-one-centimeter chip. Each element comprises a 2D MoS2transistor as well as a floating gate, used to store a charge in its memory that controls the conductivity of each transistor. Coupling processing and memory in this way fundamentally changes how the processor carries out the calculation. “By setting the conductivity of each transistor, we can perform analog vector-matrix multiplication in a single step by applying voltages to our processor and measuring the output,” explains Kis.<h2>A big step closer to practical applications</h2>The choice of material – MoS2 – played a vital role in the development of their in-memory processor. For one, MoS2 is a semiconductor – a requirement for the development of transistors. Unlike silicon, the most widely used semiconductor in today’s computer processors, MoS2 forms a stable monolayer, just three atoms thick, that only interacts weakly with its surroundings. Its thinness offers the potential to produce extremely compact devices. Finally, it’s a material that Kis’s lab knows well. In 2010, they created their first single MoS2 transistor using a monolayer of the material peeled off a crystal using Scotch tape.Over the past 13 years, their processes have matured substantially, with Migliato Marega’s contributions playing a key role. “The key advance in going from a single transistor to over 1000 was the quality of the material that we can deposit. After a lot of process optimization, we can now produce entire wafers covered with a homogenous layer of uniform MoS2. This lets us adopt industry standard tools to design integrated circuits on a computer and translate these designs into physical circuits, opening the door to mass production,” says Kis.<h2>Revitalizing European chip manufacturing</h2>Aside from its purely scientific value, Kis sees this result as a testament to the importance of close scientific collaboration between Switzerland and the EU, in particular in the context of the European Chips Act, which aims to bolster Europe’s competitiveness and resilience in semiconductor technologies and applications. “EU funding was crucial for both this project and those that preceded it, including the one that financed the work on the first MoS2 transistor, showing just how important it is for Switzerland,” says Kis.“At the same time, it shows how work carried out in Switzerland can benefit the EU as it seeks to reinvigorate electronics fabrication. Rather than running the same race as everyone else, the EU could, for example, focus on developing non-von Neumann processing architectures for AI accelerators and other emerging applications. By defining its own race, the continent could get a head start to secure a strong position in the future,” he concludes.<hr>ReferencesMarega, G. M., Ji, H. G., Wang, Z., Pasquale, G., Tripathi, M., Radenovic, A., &amp; Kis, A. (2023). Large-Scale Integrated Vector-Matrix Multiplication Processor Based on Monolayer MoS2. Nature Electronics. DOI: <a href="https://dx.doi.org/10.1038/s41928-023-01064-1" target="_blank" rel="noopener">http://dx.doi.org/10.1038/s41928-023-01064-1</a>

Developed by EPFL researchers, the first large-scale in-memory processor using 2D semiconductor materials could substantially cut the ICT sector’s energy footprint.

Redefining energy efficiency in data processing

<table border="0" cellspacing="0" cellpadding="0" width="437" id="isPasted"><tbody><tr><td><h2>At A Glance</h2></td></tr><tr><td>ChallengeInjection mould five different parts that form the casing of PRISM™ - an asset monitoring device that harnesses the principles of behavioural science – within a very tight deadline.&nbsp;SolutionCombine the efficiency of Protolabs’ quoting platform, the expertise of Protolabs’ Consultative Design Service (CDS) and the rapidity of Protolabs’ world-leading plastic injection moulding service.&nbsp;OutcomeProtolabs helped improve the design to ensure better tools and parts, provided guidance on material performance, before rapidly moulding x500 each of an inner unit cover, a lower case, a button, a clear lens for an LED to beam through and a top case.&nbsp;</td></tr></tbody></table> In collaboration with <a href="https://www.protolabs.com/en-gb/?utm_source=wevolver&utm_medium=referral&utm_campaign=uk-energy-0923&utm_content=wevolver-mindsett-cs-1023" target="_blank">Protolabs</a>, IoT solutions firm Mindsett has created PRISM™, the most intelligent asset monitoring device on the market. Mindsett is wholly owned by the Cloudfm Group, the UK’s fastest growing facilities management company, and the proud recipients of the 2019 Queens Award for Innovation. The group recently received acknowledgement in Gartner’s esteemed Market Guide for Energy Management and Optimization Systems. Mindsett have been highlighted as front runners in IoT due to their technology providing asset level intelligence that directly translates into clear, actionable data that will save the end user time, energy and money. The ground-breaking PRISM™ asset monitoring device is unique in that it harnesses the principles of behavioural science along with cutting-edge technology to not only give customers the power to save a minimum of 20% on energy bills; it helps to adjust the user’s mindset and drive long-term behavioural change around energy consumption. The Mindsett team approached Protolabs because they wanted to create an asset level energy monitor that was not only a vital and intelligent tool for energy saving, but one that harboured significant ESG credentials within its very manufacture. Initially designed with an overseas supply chain in mind to take advantage of cost efficiencies in China, the Mindsett team weren’t comfortable with the potential environmental impact and subsequently overhauled their manufacturing strategy to reshore the manufacture of PRISM™. Keen to create a more environmentally sustainable product, the Mindsett team wanted to find an agile and dynamic digital manufacturing partner who understood what they were trying to achieve within a very tight timeframe, could provide a cost-effective solution – and were based in the UK. Ian Shute, IoT Hardware manager at Mindsett had worked with Protolabs in the past and recommended that his team get in touch. As Ian explains, “Positive past experience led me back to Protolabs. We had a very aggressive timescale to meet as we needed to get the device to market quickly to fulfil significant customer demand – the cost of the energy crisis very much fuelling that demand. I knew Protolabs were responsive and believed they would rise to the challenge for us.” “We approached Protolabs quite late on in the design process, after the initial work had been done elsewhere and we were at a point where we needed performance and speed to scale into volume quickly. Initially driven by budget considerations, we previously worked with an overseas team on the prototypes, but we had several issues with this: the company that completed the industrial design of the casing, which was created as a 3D printed prototype, did not consider production by injection moulding in larger volumes, they proposed further design changes which was a frustration. We’d lost valuable time, so we turned to Protolabs due to their speed, their extensive experience with plastics and their capacity for problem solving. It was vital that we could evolve the design quickly, discuss our needs with a new supplier and achieve our aims in double-quick time. Protolabs were perfect for this.” Explaining the change of strategy, Gemma Conway, Mindsett Marketing Executive says, “Most importantly, we weren’t happy with the environmental impact and ESG implications of manufacturing our exciting new product - a device that has ultimately been created to save a huge amount of energy and help our customers become more sustainable - thousands of miles away. The irony of that didn’t sit comfortably with us.” <img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk3NDU2NTE5NDY4LTE2OTc0NTY1MTk0NjgucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 300px;" class="fr-fic fr-dib"> Along with a very tight deadline, the team had another obstacle to navigate.They needed a manufacturing partner that could support Mindsett’s engineering team who weren’t familiar with designing for plastics. So being able to tap into Protolabs’ new <a href="https://www.protolabs.com/en-gb/resources/blog/consultative-design-analysis-prepares-parts-for-production/?utm_source=wevolver&utm_medium=referral&utm_campaign=uk-energy-0923&utm_content=wevolver-mindsett-cs-1023" title="Consultative Design Analysis Prepares Parts for Production" target="_blank">Consultative Design Service (CDS)</a> was a pivotal point in the project for Mindsett. As part of their CDS service, Protolabs’ experienced injection moulding Application Engineers not only looked at making the existing CAD manufacturable, they also examined what the parts were for and helped improve the design to ensure that they could make better tools and parts.&nbsp; “We didn’t shy away from the issue. We were very open with Protolabs about our relative inexperience with development software,” explains Ian. “So we were looking to Protolabs to bring their expertise to the table and add value – without adding a nasty bill at the end. And our Protolabs Account Manager, Shay, didn’t let us down, her attention to detail was brilliant.” In total, Protolabs were tasked with <a href="https://www.protolabs.com/en-gb/services/injection-moulding/?utm_source=wevolver&utm_medium=referral&utm_campaign=uk-energy-0923&utm_content=wevolver-mindsett-cs-1023" title="Injection Moulding" target="_blank">injection moulding</a> five parts that form the casing of PRISM™: a PCBA inner unit cover, a lower case, a button, a clear lens for an LED to beam through and a top case.&nbsp;<img border="0" width="523" src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk3NDU2NDgyMTMwLTE2OTc0NTY0ODIxMzAuanBlZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" class="fr-fic fr-dii" style="width: 510px;">When it came to materials, flammability was the main concern for the Mindsett team. With Protolabs’ experience in plastics performance, the engineers were able to provide guidance to ensure that Mindsett achieved their flammability rating. After Protolabs advised on material selection, an ABS in Signal White was chosen for the exterior casing and black for the inner unit casing. “Originally we chose Pure White, wanting to move quickly, taking it on face value and assuming that we couldn’t go wrong with that,” recalls Ian. “Imagine our dismay when our initial batch of 60 in Pure White was delivered and we discovered that Pure White was actually cream. We realised that we should have double checked what shade ‘pure’ white really meant! However, Shay responded very quickly with a positive way forward. She advised us that Signal White was a standard colour that they had readily available which was lucky as we needed a run of 500 almost immediately.” There were other difficulties along the way, too. “We’d created the design files using free CAD software. This was a compromise – it enabled us to reduce the cost in the initial design phase, but it pushed inefficiencies further down the road. That said, Protolabs were very patient with us which made this later phase easier for us than it otherwise would’ve been. Their CDS service was invaluable in this respect, going back and forth with iterations to the design and giving advice on manufacturability, such as draft angles and other important geometries.” Five different tools were made for the five different parts, but the lens piece that indicates the status of the device’s Wi-Fi connectivity required additional support from Protolabs to ensure the correct intensity; CDS gave advice on different options of finishes to the lens to achieve the desired result.&nbsp;<img border="0" width="601" src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk3NDU2NDgyMTQxLTE2OTc0NTY0ODIxNDEuanBlZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" class="fr-fic fr-dii" style="width: 391px;">&nbsp;Ian explains, “When we initially had that part 3D printed, the LED light shone too brightly through the lens. We knew we needed to somehow diminish the light intensity. Protolabs were really helpful, testing different finishing options on the lens to see which one worked – and we were able to come to our conclusion quickly.” One of the final elements of the manufacturing process with Protolabs was <a href="https://www.protolabs.com/en-gb/services/secondary-services/pad-printing/?utm_source=wevolver&utm_medium=referral&utm_campaign=uk-energy-0923&utm_content=wevolver-mindsett-cs-1023" title="Pad Printing" target="_blank">industrial pad printing</a> – a printing process that transfers a 2D image onto a 3D object by using a silicone pad to transfer the image onto virtually any shaped surface. In this project, adding the Mindsett logo to the casing of PRISM™. “The file we’d created and provided for the logo was slightly offset from the embossed logo in the plastic casing part, but rather than demanding new files from us, Protolabs worked with us to resolve the issue. Tweaks they made themselves ensured accurate printing of the logo, saving us more valuable time in the process,” explains Ian. Initial contact to delivery of 500 each of the five injection moulded parts was within eight weeks – just days from order to receipt of the parts.&nbsp;<blockquote>“Shay knew how time critical these parts were for us and went over and above to make sure they were delivered when we needed them. And although we were working at speed, Protolabs didn’t make us pay a premium on price, which was really refreshing after working with other manufacturers in the past.”</blockquote>Ian Shute, IoT Hardware Manager, Mindsett&nbsp;Partnering with Protolabs and utilising their knowledge benefitted Mindsett and their PRISM device in both speed and sustainability. Protolabs used their expertise to ensure that the parts they manufactured were designed reductively for efficiencies in plastic use. Most significantly, moving manufacture of the product to the UK vastly reduced the energy requirement of the PRISM product lifecycle. These material savings, and savings on energy use throughout the product life cycle - removing the need to transport the product around the world - are important components for purchasers seeking to prop up their own ESG credentials through the industrial supply chain. Mindsett were pleased to find a digital manufacturing partner whose core values aligned with their own and who quickly understood what they were trying to achieve. Commenting on their collaboration with Protolabs, Ian concludes, “I feel that we’re building a business relationship with a team who are proactive, flexible, engaging and committed. And that doesn’t just go for Shay and Alex who we dealt with on a regular basis; it goes for the whole of the Protolabs team, even those in the background who we didn’t speak to. I felt they were all working to support us in providing us with the best possible commercial solution and speed to get our product to market.” The result of the partnership is a sleek, stylish and intelligent energy monitor that is simple to use and has sustainability at the heart of its design and manufacture. The team are proud that, with the help of Protolabs, PRISM™ is a product which comprehensively aligns with a customer’s ESG requirements whilst saving them time and money. <img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1697456913371-Mindsett--Application-Case---Image-Only-IN.png" style="width: 445px;" class="fr-fic fr-dib"> With the current energy crisis weighing heavily, it’s no surprise that this innovative energy monitoring device has enjoyed a surge of interest from businesses that include UK based SMEs and large multi-nationals. “The customer response has been fantastic,” says Gemma. “Now we have proof of concept, it’s all systems go. The interest has been incredible, which makes it all worth it.” As for the future, Mindsett will continue to work with Protolabs to keep up with demand and evolve their energy monitoring device. The team are focused on increasing sustainability as they challenge themselves to find ways to further reduce the environmental impact within the rest of their supply chain. Changing behaviour around energy consumption is not just something Mindsett can do for their customers; they want to change the mindset throughout their supplier base to help save energy, save money and ultimately make the world a better place for us all to live in.&nbsp;Want to learn more about how Protolabs supports similar businesses with CNC machined, injection moulded and 3D printed custom parts?&nbsp;<a href="https://www.protolabs.com/en-gb/resources/case-studies/?utm_source=wevolver&utm_medium=referral&utm_campaign=uk-energy-0923&utm_content=wevolver-mindsett-cs-1023" target="_blank">Click here</a>&nbsp;to check out their other case studies.

PRISM™ harnesses the principles of behavioural science along with cutting-edge technology to not only give customers the power to save a minimum of 20% on energy bills; but also help adjust  the user’s mindset and drive long-term behavioural change around energy consumption.

Protolabs helps energy saving device increase sustainability

This is the first in a series of articles, sponsored by Synopsys that look at AI, automotive, and multi-die chip design.&nbsp;<h2>Introduction</h2>Artificial intelligence (AI) accelerators are deployed in data centers and at the edge to overcome conventional von Neumann bottlenecks by rapidly processing petabytes of information. Even as Moore’s law slows, AI accelerators continue to efficiently enable key applications that many of us increasingly rely on, from ChatGPT and advanced driver assistance systems (ADAS) to smart edge devices such as cameras and sensors.Although AI accelerators are typically 100x to 1,000x more efficient than general-purpose systems, the computational resources needed to generate best-in-class AI models <a href="https://openai.com/blog/ai-and-compute/" target="_blank" id="isPasted">doubles every 3.4 months</a>. Moreover, training a single deep-learning model such as ChatGPT’s GPT3 creates approximately <a href="https://www.theguardian.com/technology/2023/jun/08/artificial-intelligence-industry-boom-environment-toll" target="_blank">500 metric tons of CO2</a>, the equivalent of over a million miles driven by an average gasoline-powered vehicle! To help reduce global carbon emissions, the U.S. Department of Energy (DoE) <a href="https://www.energy.gov/sites/default/files/2022-02/Semiconductor%20Supply%20Chain%20Report%20-%20Final.pdf" target="_blank" data-lf-fd-inspected-kn9eq4roawkarlvp="true">recently recommended</a> a 1,000x improvement in semiconductor energy efficiency.Achieving optimal performance-per-watt—whether for AI training in the data center or inference at the edge—is understandably a top priority for the semiconductor industry. In addition to minimizing environmental impact, reducing energy consumption lowers operating costs, maximizes performance within limited power budgets, and helps mitigate thermal challenges. Read on to learn how chip designers—including edge AI chip developer <a href="https://sima.ai/" target="_blank">SiMa.ai</a>—are leveraging end-to-end power analysis solutions to build a new generation of more energy-efficient AI accelerators.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk1ODA4NTA3MzQ4LWNoaXAtc3VzdGFpbmFiaWxpdHkud2VicCIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 300px;" class="fr-fic fr-dib">Semiconductors must achieve at least 1,000x improvement in energy efficiency.<section id="isPasted"><h2>Optimizing Power for Billion-Plus Gate Designs</h2>An end-to-end approach to energy efficiency for AI accelerators must start at the architectural and micro-architectural levels during the earliest stages of the design flow and conclude at signoff. That’s why AI chip designers rely on architectural exploration platforms to map and evaluate power, performance, and area (PPA) tradeoffs for specific training or inference applications while proactively identifying critical vectors for downstream analysis. As&nbsp;AI hardware typically consists of large arrays with thousands of tiles (processing elements), billion-plus-gate designs require multi-domain hardware and software power verification to minimize energy consumption and leakage. However, analyzing crucial power blocks and time windows requires advanced emulation systems to run billions of cycles and rapidly deliver multiple—and accurate—iterations. Only after completing this step can register transfer level (RTL) power analysis and physical implementation tools effectively optimize dynamic (gate switching) and static (leakage) power dissipation. To consistently deliver accurate results, RTL power analysis tools for AI chip design should include the following capabilities:<ul><li>Timing-driven fast synthesis: Internal power calculation errors are often caused by fanout-based fast synthesis tools that fail to properly size cells following timing constraints. Like their downstream place-and-route counterparts, fast synthesis embedded in RTL power analysis tools must be timing driven.</li><li>Physically aware fast synthesis: RTL power analysis tools should be “physical aware” and capable of obtaining precise net capacitance values by executing first-pass placement of the cells in the design, as well as global routing. Unlike a fanout-based approach, physically aware capacitance estimation results in a unique and accurate value for each net.</li><li>Signoff-quality power computation engine: Traditional RTL power analysis tools using word-level logic inferencing for fast synthesis can only apply heuristic—and therefore inaccurate—methods for <a href="https://www.synopsys.com/ai/what-is-glitch-power.html#:~:text=Glitches%20occur%20if%20signal%20timing%20within%20the%20paths,leading%20to%20up%20to%2040%25%20additional%20dynamic%20power%29." target="_blank">glitch power computation</a>. To accurately calculate glitch power (which can potentially consume up to 40% of a chip’s total power) and reduce highly replicated tiles, RTL power analysis tools must have a signoff-quality power analysis engine, a netlist level design representation, and an integrated timing engine.</li></ul> After completing RTL power analysis and reduction, physical implementation (synthesis and place and route) tools can be used to further optimize PPA. To ensure reliability, scalability, and a frictionless user experience, these implementation tools should include a single, integrated data model architecture, interleaved engines, and a unified shell. Just as importantly, implementation tools should be capable of accurately modeling advanced node effects and glitch power to accelerate engineering change orders (ECOs) and final design closure.</section><section><h2>Exceeding Energy-Efficiency and Performance</h2>Synopsys offers a comprehensive end-to-end power solution to help AI chip designers cost-effectively meet or exceed ambitious performance and energy-efficiency goals while<a target="_blank"></a><a target="_blank">&nbsp;</a>accelerating time to market. Used at the very beginning of the design flow, <a href="https://www.synopsys.com/verification/virtual-prototyping/platform-architect.html" target="_blank">Synopsys Platform Architect™</a> provides AI chip designers with SystemC™ transaction-level modeling (TLM) tools and efficient methods to rapidly model, analyze, and optimize complex silicon architectures.&nbsp;<a href="https://www.synopsys.com/verification/emulation.html#zebuempower" target="_blank">Synopsys ZeBu® Empower</a>, a fast power profiler, is used for the next stage of the AI chip design process: analyzing and debugging energy consumption—based on hundreds of millions of cycles—for real software workloads. Leading semiconductor companies have significantly reduced power draw with Synopsys ZeBu Empower, including&nbsp;<a href="https://sima.ai/" target="_blank">SiMa.ai</a>, a Silicon Valley-based AI chip startup that designs high-performance, low-energy AI chips for the intelligent edge. Specifically, the company realized a 2.5x frames per second (FPS) per watt improvement for its SiMa.ai™ Low Power MLSoC™. During a presentation at the SNUG Silicon Valley 2023 conference this spring, Sounil Biswas, director of silicon engineering at SiMa.ai, noted that subsequent silicon validation demonstrated excellent correlation between Synopsys ZeBu Empower data and board measurements. To complement ZeBu Empower and enable RTL design for low power, we offer&nbsp;<a href="https://www.synopsys.com/implementation-and-signoff/signoff/primepower.html" target="_blank">Synopsys PrimePower RTL</a>, an RTL power analysis and reduction tool that consistently achieves accurate results (within +/- 15% of post-route implementation) by pairing timing-driven, physically aware synthesis capabilities with an integrated computation engine. Synopsys PrimePower RTL also provides step-by-step guidance to help AI chip designers further minimize glitching and reduce overall power consumption. &nbsp; Additional PPA optimization is achieved with <a href="https://www.synopsys.com/implementation-and-signoff/physical-implementation/fusion-compiler.html" target="_blank">Synopsys Fusion Compiler™</a>, a comprehensive and integrated RTL-to-GDSII implementation system. After passing this milestone, the AI chip design is analyzed with <a href="https://www.synopsys.com/implementation-and-signoff/signoff/primepower.html" target="_blank">Synopsys PrimePower</a>, the golden power signoff solution. Certified by leading foundries worldwide down to 3nm processes, Synopsys PrimePower delivers fast runtime performance with distributed processing, achieving high accuracy at signoff within a few percent of SPICE and silicon measurements.</section><section><h2>Designing Differentiated Silicon for Edge AI Inference</h2>AI accelerators enable many popular applications to quickly analyze massive amounts of information and accurately infer results in milliseconds. At the same time, achieving optimal performance-per-watt remains a top priority for chip designers. This is especially true at the edge, where performance is often limited by minimal power envelopes and smaller die sizes. However, these constraints create new opportunities for semiconductor companies to design differentiated silicon by precisely calibrating PPA to match the specific requirements of low-latency, high-bandwidth applications. For example, autonomous navigation demands a computational response latency limit of 20μs, while voice and video assistants must understand spoken keywords in less than 10μs and hand gestures in a few hundred milliseconds. To successfully implement PPA tradeoffs, chip designers should adopt a holistic approach to power optimization by leveraging an end-to-end solution that spans early architectural exploration to golden power signoff. You can learn more about Synopsys <a href="https://wevlv.co/synopsys-energy-efficient" target="_blank" rel="noopener noreferrer">energy-efficient SoCs solutions here</a>.</section>

Leveraging Power Analysis for Energy-Efficient AI Accelerator

Designing Energy-Efficient AI Accelerators for Data Centers and the Intelligent Edge

<section id="isPasted">The Inflation Reduction Act of 2022 (IRA), which was signed into law in Q3 of 2022, is designed to maintain the United States as a global leader in clean energy technology, manufacturing, and innovation. By boosting R&amp;D, incentivizing domestic manufacturing investments, and encouraging the use of locally sourced materials, this legislation aims to enhance domestic manufacturing capacity.This initiative not only safeguards critical national security technologies, such as communications, advanced munitions, and satellite technology but also significantly contributes to American GDP and innovation. According to the Department of Defense, <a href="https://www.defense.gov/News/News-Stories/Article/Article/3189049/us-manufacturing-ecosystem-key-to-economic-growth-innovation-competitiveness/" target="_blank">US manufacturing is responsible for 55 percent of all patents</a> and 70 percent of all R&amp;D spending. Despite making up only 11 percent of the nation&rsquo;s GDP, it accounts for 60 percent of exports.Though the IRA shares some common ground with previous legislation like the Bipartisan Infrastructure Law and the CHIPS &amp; Science Act, its primary focus is on offering tax credits to businesses and individuals. These credits are intended to encourage labor improvements and training, as well as prioritize the use of clean energy. This article will concentrate on the advantages offered to businesses, rather than individual tax savings.<h2>How Does the Inflation Reduction Act Affect Businesses?</h2>The Inflation Reduction Act (H.R. 5376) is a pared-down version of the Build Back Better Act that stalled in the Senate in 2021. This legislation encompasses a variety of tax benefits and credits for manufacturing businesses across all sizes, totaling up to $216 billion, while providing the IRS with $80 billion over the next nine years to improve and expand their business systems, enforcement operations, and taxpayer services.The bill also directs nearly $400 billion to clean energy incentives, including tax incentives, grants, and loan guarantees, including over $47 billion allocated for manufacturing. It specifically encourages the manufacture of wind, battery, and solar components in the USA rather than abroad.<h2>Tax Credits Available Under the IRA</h2><h3>Research and Development Credit</h3><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg5MjUyMDEwMTA0LTE2ODkyNTIwMTAxMDQucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" alt="Research development credit" class="fr-fic fr-dii">Under previous legislation, eligible small businesses could apply a portion of their research credit as a payroll tax credit against their employer Social Security tax liability rather than their income tax liability. This has been in effect since December 31, 2015. Businesses could claim up to $250,000 of their credit for expanding research activities as a payroll tax credit against the employer&rsquo;s portion of Social Security tax, using IRS Form 8974, titled <a href="https://www.irs.gov/forms-pubs/about-form-8974" target="_blank">&ldquo;Qualified Small Business Payroll Tax Credit for Increasing Research Activities.&rdquo;</a>Now, the Inflation Reduction Act expands this credit. It allows eligible small businesses to apply an additional $250,000 in qualifying research expenses as a payroll tax credit against the employer&rsquo;s share of Medicare tax.The credit cannot exceed the tax imposed for any calendar quarter, and any unused credit amounts can be carried forward to the next quarter. This provision will be applicable for tax years starting after December 31, 2022. To qualify as a small business, certain criteria must be met, including having gross receipts below a specified threshold.<h3>Clean Energy Credit</h3><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg5MjUyMDA5MTUyLTE2ODkyNTIwMDkxNTIucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" alt="Clean energy credit" class="fr-fic fr-dii">Prior to the IRA, the Internal Revenue Code (IRC) incentivized clean energy primarily through the Section 48 investment tax credit (ITC.) This credit decreased from 26 percent in 2020-2022 to 22 percent in 2023 and was expected to fall to 10 percent from 2024 onwards.But now under the IRA, businesses have the opportunity to take advantage of investment tax credits as high as 70 percent for qualified projects, resulting in substantial potential cost savings. However, this comes with intricate requirements and starts with a base credit rate of six percent. Let&rsquo;s explore how these credits progress from there.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg5MjUyMDEwNTY3LTE2ODkyNTIwMTA1NjcucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" alt="Inflation reduction act chart" class="fr-fic fr-dii">The Inflation Reduction Act enables energy credits to be used as payment toward a taxpayer&rsquo;s tax liability. These credits are also transferable, allowing taxpayers to sell all or part of a tax credit for cash in certain cases. This feature could hold considerable importance for companies aiming to strengthen their ESG compliance.Moreover, the <a data-lf-fd-inspected-kn9eq4roawkarlvp="true" data-lf-fd-inspected-lynor8x26v54wqjz="true" href="https://www.irs.gov/pub/irs-utl/irc-179d-energy-efficient.pdf" target="_blank">IRC Sect. 179D deduction</a>, which allows building owners to deduct the cost of energy-efficient buildings or the cost of installing systems to make older buildings more energy-efficient, has risen from $1.88 to $5 per square foot. Improvements to HVAC systems, building envelopes, and interior lighting are eligible. In the case of a 100,000-square-foot building, a deduction of $500,000 could be claimed.<h2>Driving Demand in Electronics Manufacturing</h2><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg5MjUyMDA5OTMyLTE2ODkyNTIwMDk5MzIucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" alt="Ev charging technology" class="fr-fic fr-dii">As the Inflation Reduction Act offers tax credits for electric vehicle purchases, an increase in EV demand is anticipated. The IRA grants a $7,500 federal tax credit to consumers who buy electric vehicles, but only those that comply with domestic sourcing requirements, including batteries. This is likely to substantially influence domestic electronics manufacturing in various ways, including<ul><li>Increased demand for domestically sourcedhardware components, like power inverters, and AC and DC motors</li><li>An increase in EV charging technology</li><li>An increase in domestically-built solar components</li></ul><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg5MjUyMDA5OTc5LTE2ODkyNTIwMDk5NzkucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" alt="Increase solar components" class="fr-fic fr-dii">Furthermore, the low-income community tax credit (mentioned earlier) is expected to extend clean energy alternatives to rural areas where they are currently limited. This development may benefit manufacturers seeking large-scale sites. According to a&nbsp;<a href="https://www.reuters.com/markets/us/location-location-location-us-manufacturing-boom-has-real-estate-problem-2023-04-13/" target="_blank">recent Reuters report</a>, many companies considering reshoring are searching for ideal, expansive plots of land near transportation infrastructure, with access to skilled labor in the vicinity and connections to renewable energy resources. Currently, there are only a few such sites. The IRA significantly expands the Rural Energy for America Program and provides a route to bring more renewable energy resources online in rural areas where large plots of land are widely available. This may widen the pool of potential building sites for large OEMs looking to build new facilities in the USA.<h2>Potential Domestic Sourcing Challenges</h2><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg5MjUyMDEwNTEwLTE2ODkyNTIwMTA1MTAucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" alt="Potential domestic source challenges" class="fr-fic fr-dii">Adapting to the stringent domestic sourcing requirements of the Inflation Reduction Act could be a challenging task for companies that have been relying on global sourcing options. They should anticipate and be prepared for potential disruptions to their supply chains. This could involve time and resources dedicated to identifying new suppliers for components and for PCB assembly, renegotiating contracts, and managing potential issues related to product availability or mismatched delivery schedules.Shifting to domestic manufacturing partners is not a quick process. Companies typically need to evaluate several potential suppliers to find the best fit. This transition period, which can span several months, is crucial to ensure alignment between the company&rsquo;s needs and the supplier&rsquo;s facilities, capabilities, pricing, and quality standards.<h2>Small Businesses Can Cut Energy Costs</h2><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg5MjUyMDEwNjUzLTE2ODkyNTIwMTA2NTMucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" alt="Small business cut energy costs" class="fr-fic fr-dii">Finally, small business owners have multiple avenues to lower their energy costs through tax credits.As previously mentioned, companies willing to upgrade their facilities can receive a substantial tax incentive of up to $5 per square foot to support energy-efficient improvements. Further, these improvements will strengthen the long-term financial stability of the company by reducing utility costs significantly.Additionally, tax credits covering up to 30 percent of acquisition costs for clean commercial vehicles like electric and fuel cell models, are also available.<h2>How MacroFab Can Help</h2><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg5MjUyMDEwMTQ4LTE2ODkyNTIwMTAxNDgucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" alt="How macrofab can help pcba board" class="fr-fic fr-dii">MacroFab is based in Houston, with a factory network located entirely in North America. We have strong partnerships with domestic sourcing options for your necessary electronic components. Let our team help you navigate Buy America regulations to ensure the most lucrative tax benefits for your project.The energy industry will be increasingly compelled to embark on ambitious projects with the advent of this legislation, and MacroFab has the factory capacity to meet its needs, and with over 100 factory lines available, it&rsquo;s easy to find the right fit. If your company needs additional manufacturing resources, reach out to our team.MacroFab offers a flexible, innovative approach to product development, so you can get to market faster and with higher quality, while aligning with an American company that shares your values and quality standards. With MacroFab&rsquo;s services, you can support domestic manufacturing while enjoying the benefits of Made in America excellence and the financial incentives provided by the Inflation Reduction Act.<h4>Our Experts Are Here To Help</h4><a href="https://www.macrofab.com/contact/" target="_blank">SCHEDULE A MEETING WITH A MACROFAB EXPERT</a></section><section> </section>

The Inflation Reduction Act directs nearly $400 billion to clean energy incentives including $47 billion allocated for manufacturing.  This article concentrates on the advantages offered to businesses. 

Energy optimization

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