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

Bluetooth&nbsp;LE, Wi-Fi&nbsp;6,&nbsp;and cellular IoT bring connectivity to low power IoT devices. The Nordic SoCs, companion ICs, and SiPs running these protocols are among the most power efficient wireless solutions on the market, extending devices’ battery life.&nbsp;But there’s more to managing power than just efficient chips; power from a battery or a mains source must be regulated and distributed, and if the device is powered by a rechargeable battery, it will occasionally need to be charged. That means no matter how efficient the processor, radio, and memory are in the wireless chip, a less-than-optimized power management system will compromise battery life.&nbsp;&nbsp;<h2>Building a compact power management system&nbsp;Into&nbsp;one small package&nbsp;</h2>Building an efficient power management system is one problem, designing it to be compact makes things even more challenging. Many IoT products are space constrained, yet conventional power management solutions typically comprise multiple chips—for example, voltage regulator, battery charger, fuel gauge, external watchdog, and hard reset—which take up precious space.&nbsp;Nordic has addressed both the efficiency and space challenges of power management with its nPM Family of&nbsp;<a href="https://www.nordicsemi.com/Products/Supplementary/Power-Management" target="_blank">Power Management ICs</a>&nbsp;(PMICs). The family comprises the nPM6001 (which features six independently controlled power rails), nPM1100 (a dual-mode configurable buck regulator and integrated battery charger) and the latest introduction, the nPM1300. This PMIC is optimized for maximum efficiency and compact size and simplifies system design by integrating the essential functions required for reliable operation into one small package.&nbsp;<h2>Intelligent power management from the nPM1300&nbsp;</h2>Within its 5 by 5 mm (QFN32) or 3.1 by 2.4 mm (WLCSP) compact form factor, the nPM1300 incorporates two buck converters, two load switches, a battery charger, USB-C compatible input, ship mode, and fuel gauge. This simplifies power management from as many as eight chips plus associated passives to one chip plus a few passives.&nbsp;In addition to the high level of integration, the PMIC provides key system management features that typically must be individually added to conventional power management circuits. These features include key items such as failed boot recovery, hard reset, a power saving ‘hibernate’ mode, watchdog timer, and a power loss warning.&nbsp;<h2>Precise fuel gauging without the power drain&nbsp;</h2>It’s very useful for a consumer to have an accurate idea of the remaining battery life of their IoT device. Engineers typically achieve this by measuring the battery voltage—which is a proxy for remaining energy—or, for a more precise result, a coulomb counter, a device that continuously measures the current flowing into or out of a battery to determine the total cell charge.&nbsp;&nbsp;The battery voltage measurement is simple to implement and uses little power, but is not very accurate, particularly if the battery is subject to temperature variances. The coulomb counter is more precise but requires additional components and drains more power. In comparison, the nPM1300 features a highly accurate fuel gauge that combines the simplicity and modest power consumption of a battery voltage measurement with the precision of a coulomb counter.&nbsp;&nbsp;Nordic conducted experiments that compared a coulomb counter, battery voltage measurement, and the nPM1300’s fuel gauge. Compared to the coulomb counter, the battery voltage calculation peaked at nearly 20 percent error. In comparison, the nPM1300’s fuel gauge showed a maximum of two percent error at worst compared with the coulomb counter but was mostly within one percent.&nbsp;&nbsp;<h2>Fuel gauge powered by software&nbsp;</h2>The key to Nordic’s fuel gauge’s accuracy over a wide temperature range is the software algorithm powered by the host processor. The processor uses information such as the current flowing out of the battery, the battery terminal voltage, the system voltage, and a battery temperature thermistor reading.&nbsp;&nbsp;For the algorithm to produce accurate results it must first be ‘taught’ the battery characteristics (which only has to happen once if the end product continues to use the same type of battery). The teaching is done using the Nordic&nbsp;<a href="https://www.nordicsemi.com/Products/Development-hardware/nPM1300-EK" target="_blank">Evaluation Kit</a>&nbsp;(EK) equipped with a fuel gauge board to provide the right resistive load and the Nordic nPM PowerUP App.&nbsp;After calibration and with the current, voltage, and temperature inputs, the host processor uses the Nordic-developed algorithm to accurately determine the battery’s state of charge for the life of the device. Power consumption for a one-second measurement frequency adds about four microamps to the typical host processor overhead.&nbsp;<h2>Managing power from&nbsp;battery to antenna&nbsp;</h2>Maximizing battery life demands taking responsibility for how each part of the wireless product’s system affects the overall power consumption. The developer needs to optimize each element—from the battery all the way to Cloud connectivity, including radio operation, processing, and power management—to get the most from the power source. &nbsp;&nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;Basing a design on a Nordic SoC or SiP—among the lowest power wireless chips on the market—together with a Nordic nPM6001, nPM1100, or nPM1300 is an excellent foundation for keeping the energy consumption of wireless products as low as possible.&nbsp;

Bluetooth LE, Wi-Fi 6, and cellular IoT bring connectivity to low power IoT devices. The Nordic SoCs, companion ICs, and SiPs running these protocols are among the most power efficient wireless solutions on the market, extending devices’ battery life. 

Inside the next generation of efficient and compact power management

Renewable energy sources, such as solar, wind, and hydroelectric power, play a crucial role in the global energy transition towards a more sustainable and ecological energy scenario. As the demand for renewable energy grows, so does the need for technologies that can improve the management of these resources and the transformation efficiency. One critical aspect of ensuring the viability of renewable energy projects is applying effective risk management solutions to manage generating equipment, avoiding unwanted interruptions, and optimizing system operation. In this context, single board computers (SBCs) are emerging as compact and versatile computing devices that offer technical advantages in the renewable energy sector, supporting the operation and management of renewable energies.This article explores how SBCs can be used in renewable energy applications, integrating system monitoring and control.<h1 dir="ltr">SBCs in Renewable Energy</h1>SBCs, with their compact size and powerful hardware, offer features that make them attractive for renewable energy applications, where real-time data analysis, monitoring, and control are essential. SBCs differ from conventional computing solutions in providing flexibility, such as being installed where space may be limited presenting different connectivity options with sensors and devices. They are compatible with various operating systems, making them highly customizable and adaptable in the applications. Moreover, the SBCs offer low application costs.<h2 dir="ltr">System monitoring</h2>Continuous monitoring of the performance of renewable energy installations, such as solar farms or wind turbines, is essential to monitor energy generation and ensure the effective use of the system. For this purpose, SBCs communicating with various sensors can be strategically placed throughout these installations to collect data about multiple parameters, including electricity production, equipment temperatures, and weather conditions. This allows grid operators to assess the system's functioning in real-time and promptly identify potential problems.<h2 dir="ltr">Grid balance</h2>The integration of renewable energy into the electric grid introduces fluctuations due to the intermittent nature of these energy sources. SBCs can contribute to grid stability by optimizing the distribution of renewable energy through the data collected and running algorithms for this purpose. These devices can analyze energy supply and demand in real-time, allowing automatic adjustments to energy production from renewable sources or the activation of energy storage systems. The usage of dynamic grid management ensures a reliable energy supply, balance generation and consumption in real-time.<h2 dir="ltr">Predictive maintenance</h2>Conventional systems typically use periodic maintenance programs, which may sometimes be less efficient and potentially costly. In contrast, when integrated with SBCs, equipment can undergo continuous monitoring and have its data analyzed for any potential anomalies. Using machine learning algorithms and artificial intelligence, SBCs can accurately predict when maintenance is required. This proactive approach helps reduce downtime, significantly cut maintenance costs, and extend the operational lifetime of renewable energy assets.<h1 dir="ltr">Benefits and Challenges of SBCs in Energy Generation</h1>Due to their flexibility and powerful processing capabilities, SBCs can improve various aspects of renewable energy generation:<ul><li dir="ltr">Energy reliability:&nbsp;SBCs can provide real-time insights into performance and potential equipment issues, contributing to the overall reliability.</li><li dir="ltr">Rapid interventions:&nbsp;Through data analysis and instant alerts, SBCs enable rapid responses to critical events or damage, minimizing energy loss and the impact on profits. For example, overheating of equipment or anomalies in the operation of photovoltaic or wind power generation.</li><li dir="ltr">Cost-effective:&nbsp;The predictive maintenance and optimized network balancing that can be done using SBCs reduce operating and maintenance costs. Furthermore, SBCs have compact sizes and low power consumption.</li></ul>However, there are some challenges in implementing it in renewable energy resources:<ul><li dir="ltr">Initial investment:&nbsp;Implementing SBC-based systems can require significant initial investments in software and integration with existing renewable energy infrastructure.</li><li dir="ltr">Technical expertise:&nbsp;Integrating sensors, data analysis software, and control systems is complex and can present technical challenges, requiring expertise in renewable energy and computing.</li><li dir="ltr">Cybersecurity:&nbsp;As renewable energy systems become more connected, they are susceptible to cyber-attacks, and ensuring the security of SBC-based systems is vital.</li></ul><h1 dir="ltr">Case Study: SBCs for Photovoltaic Generation</h1>SBCs can be utilized in photovoltaic systems to monitor and control energy production. They can collect data from solar panels and control their operation. An SBC, such as ROCK 5B, can receive and process data to help optimize photovoltaic system efficiency and generation. ROCK 5B, developed by <a href="https://www.okdo.com/" target="_blank">OKdo</a> in collaboration with <a href="https://radxa.com/" target="_blank">Radxa</a>, is a state-of-the-art device powered by the Rockchip RK3588 SoC, Arm DynamIQ Quad Cortex-A76 @ 2.2/2.4 GHz. It features a safe shutdown on/off button and is compatible with various operating systems, including Linux and Android, for enhanced application flexibility. The ROCK 5B has extensive storage capacity, with both 8 and 16 GB of RAM options as well as a high-speed eMMC socket for eMMC modules - ideal for storing operating systems and data. Additionally, it has high compatibility with SBC accessories via the 40-pin GPIO expansion connector.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk5MjY5NDg5NDE1LWltYWdlMi5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib">ROCK 5B. Image credit: OKdo.Photovoltaic systems use current and voltage sensors to monitor energy generation. Some of the popular sensors available in the market include the ACS712 for measuring electric current and the INA219 for measuring the voltage of the panel array. Real-time monitoring of photovoltaic performance is possible with these sensors, which provide valuable information for control and preventive maintenance. Temperature sensors such as PT100 and solar radiation sensors like LI-200R can be used to monitor the operating conditions of solar panels and optimize system efficiency. For example, photovoltaic generation is directly related to the solar irradiation incident on the solar panels, making irradiation an excellent metric for monitoring energy generation. The relationship between the two on a clear day is illustrated in the figure below.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk5MjY5NTE1NTk1LWltYWdlMS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib">Example of photovoltaic generation vs solar irradiation. Image credit: [9]Additionally, a web server like Apache can remotely monitor the solar energy system. The data collected and processed by the SBC can be easily accessible to interested parties, such as solar power plant operators and owners.<h2 dir="ltr">Other applications</h2>Wind turbine operators can utilize SBCs to monitor vibrations, temperature, and energy production continuously. With this information, the maintenance algorithms can provide valuable insights for predictive maintenance and reduce downtime.&nbsp;<iframe id="639e88944155b50008d4cdf8" width="250" class="specs-iframe" height="440" src="https://wevolver.com/iframe/specs/okdo-x-radxa-rock-5-model-b?iframe=true" frameborder="0" scrolling="false" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>Another potential application of SBCs would be in smart grids, which can be employed to manage energy storage and distributed energy resources. By optimizing energy distribution and storage, this technology would enable demand response and efficient energy supply.<h1 dir="ltr">Future Perspectives on Using SBCs in Renewable Energy Systems</h1>As technology advances, the role of SBCs in the renewable energy sector is set for considerable expansion. With the Internet of Things (IoT) and Artificial Intelligence (AI) becoming increasingly prevalent, SBCs can serve as a promising element in increasing the efficiency and safety of renewable energy systems. The technical characteristics of SBCs position them as a powerful device for ensuring the viability and sustainability of renewable energy solutions.<h1 dir="ltr">Conclusion</h1>SBCs contribute to harnessing the potential of renewable energy by enabling real-time monitoring, grid balancing, and predictive maintenance. These devices offer flexibility and economy, benefiting reliability, rapid response to problems, and cost savings. Although there are challenges, SBCs show great potential for the future as technology develops, supporting more efficient and sustainable renewable energy systems.<h1 dir="ltr">References</h1><ol><li dir="ltr">Philip Hirschhorn and Tom Brijs, 2021. “<a href="https://www.bcg.com/publications/2021/addressing-variable-renewable-energy-challenges" target="_blank">Rising to the Challenges of Integrating Solar and Wind at Scale</a>”</li><li dir="ltr">IRENA, 2015. “<a href="https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2015/IRENA_Baseload_to_Peak_2015.pdf" target="_blank" data-lf-fd-inspected-kn9eq4roawkarlvp="true">From Baseload to Peak: renewables provide a reliable solution</a>”</li><li dir="ltr">Kabeyi Moses Jeremiah Barasa and Olanrewaju Oludolapo Akanni, 2022. “<a href="https://www.frontiersin.org/articles/10.3389/fenrg.2021.743114" target="_blank">Sustainable Energy Transition for Renewable and Low Carbon Grid Electricity Generation and Supply</a>”</li><li dir="ltr">Johns Hopkins University, 2021. “<a href="https://energy.sais.jhu.edu/articles/the-future-of-sustainable-energy/" target="_blank">The Future of Sustainable Energy</a>”</li><li dir="ltr">Hadi Ganjineh, 2021. “<a href="https://www.forbes.com/sites/forbestechcouncil/2021/11/30/how-artificial-intelligence-and-machine-learning-are-transforming-the-future-of-renewable-energy/?sh=7f961d5e541b" target="_blank">How Artificial Intelligence And Machine Learning Are Transforming The Future Of Renewable Energy</a>”</li><li dir="ltr">Nicolas Sarmiento Sierra, 2023. “<a href="https://www.wevolver.com/article/enhancing-electrical-grid-management-with-emerging-sbc-technology" target="_blank">Enhancing Electrical Grid Management with emerging SBC technology</a>”</li><li dir="ltr">R H P Putra and D Wahyudin and T Sucita, 2018. “<a href="https://iopscience.iop.org/article/10.1088/1757-899X/384/1/012041" target="_blank">Designing Energy and Power Monitoring System on Solar Power Plant Using Raspberry Pi</a>”</li><li dir="ltr">Hamzah Eteruddin and David Setiawan and Atmam, 2020. “<a href="https://iopscience.iop.org/article/10.1088/1755-1315/469/1/012051" target="_blank">Web Based Raspberry Monitoring System Solar Energy Power Plant</a>”</li><li dir="ltr">Muhammad Qamar Raza, Mithulananthan Nadarajah and Chandima Ekanayake, 2016. “<a href="https://www.researchgate.net/publication/305321699_On_recent_advances_in_PV_output_power_forecast" target="_blank">On recent advances in PV output power forecast</a>”</li></ol>

How single board computers (SBCs) are shaping and helping to manage and mitigate risks in renewable energy


Impact of SBCs in Renewable Energy

The new iteration features groundbreaking silicon at its heart. The <a href="https://www.mouser.com/new/raspberry-pi/raspberry-pi-5-sbc/" target="_blank">Raspberry Pi 5</a> is a game-changer with a few new tricks up its sleeve.The Raspberry Pi Foundation has navigated countless challenges over the years, from production woes to supply chain disruptions. But their unwavering commitment to delivering computing innovation to all has brought us to this pivotal moment.<h2 dir="ltr">Introducing the Raspberry Pi 5: A Marvel of Custom Silicon</h2>The Raspberry Pi 5 is based on the Broadcom BCM2712, which houses a quad-core 2.4GHz 64-bit Arm® Cortex®-A76 processor. However, the standout component here is the RP1, a proprietary I/O controller crafted entirely in-house by the Raspberry Pi team. This custom chip assumes a pivotal role in furnishing USB 3.0, Ethernet, GPIO, MIPI display, and MIPI camera ports. The inclusion of the RP1 custom I/O chip not only adds stability to the Pi 5's manufacturing process but also optimizes power consumption by integrating only the most critical circuits into the chip.&nbsp;&nbsp;<iframe id="6567366c3a755eabfe69da44" width="250" class="specs-iframe" height="440" src="https://wevolver.com/iframe/specs/raspberry-pi-5-single-board-computer?iframe=true" frameborder="0" scrolling="no" data-gtm-yt-inspected-9="true" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>But the excitement doesn't stop there. Beyond custom silicon, the Raspberry Pi 5 introduces a four-lane PCIe 2.0 interface, streamlining high-speed peripheral connections. It also boasts Bluetooth® 5.0 with Bluetooth Low Energy, dual-band Wi-Fi®, and Gigabit Ethernet, granting users the flexibility to choose between wired and wireless connectivity. Moreover, the Pi 5 extends support for Power over Ethernet (PoE), enabling users to power the board via Ethernet, alongside a USB Type-C port for alternative power sources.&nbsp;While the board seamlessly integrates a real-time clock (RTC), the absence of an onboard battery necessitates using a networked time server to maintain time accuracy during power disruptions. Fortunately, users can power the RTC externally through a supercapacitor or small battery, ensuring precise timekeeping even in power outage scenarios.&nbsp;Power management receives an upgrade with the Renesas DA9091 power management IC, capable of supplying up to 20A to the BCM2712 for high-performance processing. The PMIC and Raspberry Pi 27W power supply should reduce or eliminate power warnings in most scenarios.<h2 dir="ltr">Raspberry Pi 5 Specifications</h2><ul><li dir="ltr">Broadcom BCM2712 2.4GHz quad-core 64-bit Arm Cortex-A76 CPU featuring cryptography extensions, 512KB per-core L2 caches, and a 2MB shared L3 cache.</li><li dir="ltr">VideoCore VII GPU, supporting OpenGL ES 3.1, Vulkan 1.2</li><li dir="ltr">Dual 4Kp60 HDMI display output with HDR support</li><li dir="ltr">4Kp60 HEVC decoder</li><li dir="ltr">LPDDR4X-4267 SDRAM (4GB and 8GB available at launch)</li><li dir="ltr">Dual-band 802.11ac Wi-Fi</li><li dir="ltr">Bluetooth 5.0/Bluetooth Low Energy</li><li dir="ltr">microSD card slot, with support for high-speed SDR104 mode</li><li dir="ltr">2 × USB 3.0 ports, supporting simultaneous 5Gbps operation</li><li dir="ltr">2 × USB 2.0 ports</li><li dir="ltr">Gigabit Ethernet, with PoE+ support (requires separate PoE+ HAT)</li><li dir="ltr">2 × 4-lane MIPI camera/display transceivers</li><li dir="ltr">PCIe 2.0 x1 interface for fast peripherals (requires separate M.2 HAT or other adapter)</li><li dir="ltr">5V/5A DC power via USB Type-C, with Power Delivery support</li><li dir="ltr">Raspberry Pi standard 40-pin header</li><li dir="ltr">Real-time clock, powered from external battery</li><li dir="ltr">Power button</li></ul><h2 dir="ltr">Conclusion</h2>The introduction of the Raspberry Pi 5 heralds yet another innovation from the Raspberry Pi Foundation. The inclusion of the RP1 custom I/O chip not only adds stability to the Pi 5's manufacturing process but also optimizes power consumption by integrating only the most critical circuits into the chip.&nbsp;<a href="https://www.mouser.com/new/raspberry-pi/raspberry-pi-5-sbc/" target="_blank">You can pre-order the Raspberry Pi 5, from Mouser here.&nbsp;</a>Note: This article was written by Mouser staff using OpenAI ChatGPT v4.

The Raspberry Pi Foundation just released their latest innovation, the Raspberry Pi 5.

Meet the new Raspberry Pi 5

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

Qualcomm's Pioneering Approach to Hybrid AI: Merging Cloud Processing with On-Device Efficiency and Scale


Balancing the Cloud and the Edge: A Close look at Enabling Hybrid AI

Sensors to monitor and regulate indoor air quality can have a positive impact on home, work, and school environments. Image Source: EPA 

Air Quality Indexes (AQIs) are used to measure the concentration of pollutants like particulate matter and other harmful substances in the air outside. 

Breathing Better: Using Environmental Sensors to Improve Indoor Air Quality

Single-board computers are an increasingly attractive option to shift electricity grid management towards more advanced and less resource consuming smart grids.


Enhancing Electrical Grid Management with emerging SBC technology

If you&rsquo;re developing an IoT device, odds are that you want it to do some valuable computations to solve an important problem. Maybe you want to <a href="https://www.mistywest.com/posts/5-reasons-to-use-an-iot-framework/" target="_blank">deploy sensors in remote locations</a>, develop a device that can perform data analytics to monitor a renewable energy source, or build a medical device that can use computer vision to detect early signs of an illness.Whatever you&rsquo;re building, at some point you may start to wonder: should your device perform these important computations in the cloud or at the edge? Choosing between computing on the cloud or on the edge is a decision that can impact things like your device&rsquo;s cost or efficiency &ndash; and no one wants to make the wrong decision initially and spend time and money later down the line to pivot to the correct one.&nbsp;We asked some of our experienced engineers what they think someone needs to consider when choosing between computing in the cloud or on the edge.&nbsp;<h2>What is cloud computing?</h2>The &ldquo;cloud&rdquo; refers to the collection of servers that can be accessed over the internet &ndash; popular <a href="https://www.mistywest.com/posts/azure-vs-aws-which-iot-framework-is-right-for-you/" target="_blank">cloud providers include Amazon Web Services, Microsoft Azure</a>, and Google Cloud.&nbsp;These servers can provide on-demand computing resources to store and process data. You can think of the cloud as a centralized location for your files and programs, and you can connect any device to the cloud to access them. Services like Dropbox or Google Drive are some of the many cloud-based services out there.&nbsp;<img data-fr-image-pasted="true" src="https://www.mistywest.com/wp-content/uploads/2022/05/MistyWest_Edge_Vs_Cloud-03.png" alt="" width="100%" height="auto" srcset="https://www.mistywest.com/wp-content/uploads/2022/05/MistyWest_Edge_Vs_Cloud-03.png 1360w, https://www.mistywest.com/wp-content/uploads/2022/05/MistyWest_Edge_Vs_Cloud-03-300x155.png 300w, https://www.mistywest.com/wp-content/uploads/2022/05/MistyWest_Edge_Vs_Cloud-03-1024x528.png 1024w, https://www.mistywest.com/wp-content/uploads/2022/05/MistyWest_Edge_Vs_Cloud-03-768x396.png 768w" sizes="(max-width: 1360px) 100vw, 1360px" class="fr-fic fr-dii">&nbsp;Cloud computing describes the idea of performing computations in the cloud. These computations can include data analysis and visualization, computer vision and machine learning. An example of cloud computing in action is when your average smart home speaker sends your audio input to the cloud where it is interpreted by algorithms and sends back a response. &nbsp;&nbsp;&nbsp;<h2>What is edge computing?</h2><a href="https://blogs.nvidia.com/blog/2022/01/05/difference-between-cloud-and-edge-computing/#:~:text=The%20main%20difference%20between%20cloud,deploy%20them%20at%20the%20edge" target="_blank">The edge describes the &ldquo;edge&rdquo; of the network.&nbsp;</a>It includes devices that are an entry/exit point to the cloud, but are not part of the cloud itself. For example, a server in a data centre is part of the cloud; your smartphone and router that connects to that server is part of the edge.&nbsp;&nbsp;<img data-fr-image-pasted="true" src="https://www.mistywest.com/wp-content/uploads/2022/05/MistyWest_Edge_Vs_Cloud-02.png" alt="" width="100%" height="auto" srcset="https://www.mistywest.com/wp-content/uploads/2022/05/MistyWest_Edge_Vs_Cloud-02.png 1360w, https://www.mistywest.com/wp-content/uploads/2022/05/MistyWest_Edge_Vs_Cloud-02-300x155.png 300w, https://www.mistywest.com/wp-content/uploads/2022/05/MistyWest_Edge_Vs_Cloud-02-1024x528.png 1024w, https://www.mistywest.com/wp-content/uploads/2022/05/MistyWest_Edge_Vs_Cloud-02-768x396.png 768w" sizes="(max-width: 1360px) 100vw, 1360px" class="fr-fic fr-dii">&nbsp;Edge computing describes the idea of performing computations on the edge. This way, the processing is done closer to or at the location where the data is collected or acted upon.An example of an edge computing process is object detection on an autonomous vehicle. The vehicle processes data from its sensors and uses the results to avoid obstacles. Unlike your smart home speaker, the data it collects is processed locally, rather than sending it to the cloud.&nbsp;&nbsp;<h1>Key Considerations</h1>There are a couple of key questions to consider when choosing between the edge and cloud computing.&nbsp;<h2>What is the quality of your device&rsquo;s network?</h2>Performing computations on the cloud can work well when you have high-bandwidth, low-latency, and stable connection to the internet, as you will be needing to send your data back and forth between cloud servers and your device. If your device is intended to be used, for instance, in a home or office with a good internet connection, this back and forth can be done relatively seamlessly.&nbsp;In most cases, if the computation is done on the edge, it won&rsquo;t be affected by poor or lost internet connection in a remote location; the processing can continue since it is not computed in the cloud. You wouldn&rsquo;t want your vehicle&rsquo;s object detection to stop working while on a long road trip; that&rsquo;s one of the reasons why autonomous vehicles frequently perform computations like object detection on the edge.&nbsp;&nbsp;<h2>How quick and how often does your data need to be processed?</h2>Edge computing can be ideal in cases where your customer needs response times from your device to be faster than what can be achieved with a decent network connection, such as monitoring vital components of a system. The latency from travel-time between the device and the cloud can be reduced or eliminated completely. As a result, the data can be processed right away. If the data processing itself is quick, you could achieve real-time responses from your device.&nbsp;Cloud computing is beneficial when device use is intermittent. Smart homes devices are a good example of this again, where running computations in the cloud lets you share the same computing resources between multiple customers. This reduces costs by avoiding the need to provision your device with upgraded hardware to run the data processing.&nbsp;&nbsp;<img data-fr-image-pasted="true" src="https://www.mistywest.com/wp-content/uploads/2022/05/MistyWest_Edge_Vs_Cloud-05.png" alt="" width="100%" height="auto" srcset="https://www.mistywest.com/wp-content/uploads/2022/05/MistyWest_Edge_Vs_Cloud-05.png 1360w, https://www.mistywest.com/wp-content/uploads/2022/05/MistyWest_Edge_Vs_Cloud-05-300x155.png 300w, https://www.mistywest.com/wp-content/uploads/2022/05/MistyWest_Edge_Vs_Cloud-05-1024x528.png 1024w, https://www.mistywest.com/wp-content/uploads/2022/05/MistyWest_Edge_Vs_Cloud-05-768x396.png 768w" sizes="(max-width: 1360px) 100vw, 1360px" class="fr-fic fr-dii">&nbsp;<h2>What part of your data is important to you?</h2>Computing on the edge is useful if you only care about the result of your data after it has been processed. You can send only what is important to store in the long-term in the cloud, and doing so will allow you to reduce the cost of storing and processing data in the cloud. For example, if you are creating a traffic surveillance device that needs to report levels of congestion on a road, you could preprocess the videos on the edge&ndash;Instead of running hours of raw video in the cloud&ndash;and only send images or clips of the traffic when traffic is present.It&rsquo;s possible you need to keep the data to <a href="https://www.mistywest.com/posts/improving-dataset-creation-for-machine-learning/" target="_blank">build out your machine learning dataset</a>, or you plan on analyzing the raw data in other ways in the future. If you are already sending your raw data to the cloud, it may be ideal to perform calculations in the cloud as well.&nbsp;<h2>What are your device&rsquo;s power and size limitations?</h2>If you expect your device will be restricted in power and size, given that it has a good network connection, sending the computing work to be done on the cloud will allow your device to remain small and low-power. Google Home and Amazon Alexa, for example, will capture the audio and send it to the cloud for processing, allowing complex computations to be run on the audio that would not be possible to run on the small computers inside the devices themselves.&nbsp;<img data-fr-image-pasted="true" src="https://www.mistywest.com/wp-content/uploads/2022/05/MistyWest_Edge_Vs_Cloud-04.png" alt="" width="100%" height="auto" srcset="https://www.mistywest.com/wp-content/uploads/2022/05/MistyWest_Edge_Vs_Cloud-04.png 1360w, https://www.mistywest.com/wp-content/uploads/2022/05/MistyWest_Edge_Vs_Cloud-04-300x155.png 300w, https://www.mistywest.com/wp-content/uploads/2022/05/MistyWest_Edge_Vs_Cloud-04-1024x528.png 1024w, https://www.mistywest.com/wp-content/uploads/2022/05/MistyWest_Edge_Vs_Cloud-04-768x396.png 768w" sizes="(max-width: 1360px) 100vw, 1360px" class="fr-fic fr-dii">&nbsp;<h2>Is your data processing model your Intellectual Property?</h2>If you are making a consumer device and the method you are using to process data is part of your Intellectual Property (IP), you may need to consider how you plan to protect it. Putting your IP on your device without a robust security plan can leave it vulnerable to hacks. If you don&rsquo;t have the knowledge or resources to secure your IP on the edge, it may be best to leave it on the cloud, which already has security measures in place.&nbsp;<a data-rel="lightbox-image-0" data-rl_caption="" data-rl_title="" href="https://www.mistywest.com/wp-content/uploads/2022/05/MistyWest-Cloud-Vs-Edge-FlowChart-01.jpg" target="_blank" title=""><img data-fr-image-pasted="true" src="https://www.mistywest.com/wp-content/uploads/2022/05/MistyWest-Cloud-Vs-Edge-FlowChart-01.jpg" alt="" width="100%" height="auto" srcset="https://www.mistywest.com/wp-content/uploads/2022/05/MistyWest-Cloud-Vs-Edge-FlowChart-01.jpg 1801w, https://www.mistywest.com/wp-content/uploads/2022/05/MistyWest-Cloud-Vs-Edge-FlowChart-01-275x300.jpg 275w, https://www.mistywest.com/wp-content/uploads/2022/05/MistyWest-Cloud-Vs-Edge-FlowChart-01-938x1024.jpg 938w, https://www.mistywest.com/wp-content/uploads/2022/05/MistyWest-Cloud-Vs-Edge-FlowChart-01-768x839.jpg 768w, https://www.mistywest.com/wp-content/uploads/2022/05/MistyWest-Cloud-Vs-Edge-FlowChart-01-1406x1536.jpg 1406w" sizes="(max-width: 1801px) 100vw, 1801px" class="fr-fic fr-dii"></a>&nbsp;<h2>Some final considerations for choosing between edge and cloud computing</h2>There are quite a few things to consider when choosing between computing on the edge or in the cloud. In complex problems, you may benefit from using a combination of both, by leaving some parts of your processing on the edge and the rest on the cloud.We created the above flow chart to help guide you through which path you should take, but there may always be more to consider for your device, depending on how complex your use case is.&nbsp; This article was originally written by Amanda Luu, former Product Design Engineer Coop at MistyWest,&nbsp;<a href="https://www.mistywest.com/posts/edge-vs-cloud-computing-which-solution-is-better-for-your-connected-device/" rel="noopener noreferrer" target="_blank">for the MistyWest blog</a>.

Choosing between computing on the cloud or on the edge is a decision that can impact things like your device’s cost or efficiency. Here’s how you can ensure you make the right decision early on in your product development roadmap.

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