BrainChip Podcast: Exploring Neuromorphic Computing with Industry Expert Michael Azoff
In this episode, Rob Telson speaks with Industry Expert, Michael Azoff, about neuromorphic computing in the AI space including edge AI, neural network architectures, sensory-based applications and more.
19 Oct, 2023. 3 min read
In a recent episode of the BrainChip podcast, Rob Telson, the Vice President of Sales and Marketing at BrainChip, spoke to Michael Azoff, an industry analyst with a focus on AI at the edge. He has 18 years of experience as an analyst and has previously worked hands-on with neural networks. We highlight some of the key discussion points below but encourage you to listen to the whole episode here.
A Dive into the Past and Present
During the podcast, Azoff recounted his extensive experience with AI, which began in the 1990s. He reminisced about the early days of neuromorphic computing and how it was just taking root. As the conversation progressed, he highlighted the significant milestones, emphasizing the advancements in training neuromorphic architecture-based neural networks. He also acknowledged BrainChip's notable ascent in recent years, pointing to it as evidence of the growing momentum in the neuromorphic field.
Neuromorphic's Niche in Edge AI
Azoff's recent contributions to Gigaom's reports have centered on Edge AI, a field that emphasizes inferencing at the edge. In his analysis, the Edge AI domain bifurcates into three distinct categories: ultra-low power, low power, and automotive. Intriguingly, neuromorphic architectures have carved a niche for themselves in the ultra-low power segment. Their inherent efficiency and minimal power consumption make them a natural fit for this category.
Applications: From Surveillance to Automotive
Neuromorphic architectures excel in scenarios characterized by sparse data. For instance, in visual surveillance, where a static scene is occasionally interrupted by noteworthy events, these architectures prove invaluable. Their design allows them to operate in close proximity to sensors, processing data in real-time. The applications are diverse: smart doorbells, earbuds, industrial fault detection, and even monitoring crowd density at train stations. While vision and audio remain the primary focus areas, the potential applications are vast.
In the automotive sector, a distinction emerges between in-cabin applications and driving functionalities. While the latter, especially autonomous driving, demands robust processing, neuromorphic computing finds its place in the former. In-cabin applications, ranging from infotainment to human-machine communication, can benefit immensely from neuromorphic's capabilities.
Consumer Electronics: The Next Frontier
As neuromorphic technology matures, its integration into consumer products is anticipated to grow. The allure of low power consumption cannot be overstated. For application developers, this presents a golden opportunity to innovate, pushing the boundaries of what's possible. In a decade, we might witness a paradigm shift where handheld devices and consumer electronics come equipped with neuromorphic processors, either as accelerators or integral components of the system's ecosystem.
AI's Societal Impact and the Dream of Understanding the Human Brain
Beyond applications, the podcast touched upon a profound topic: the beneficial impact of AI on society. AI's prowess lies in its ability to automate tasks that surpass human capabilities, especially when scale and complexity come into play. The horizon is vast, with the ultimate goal being the enhancement of AI architectures' intelligence, inching closer to the elusive general artificial intelligence.
Azoff's vision for an AI superpower is both ambitious and profound: to unravel the mysteries of the human brain. If AI can aid in understanding our brain's intricacies, the implications for neuroscience, psychology, and AI itself could be groundbreaking.
In Conclusion
This episode of BrainChip's podcast offers a panoramic view of neuromorphic computing's past, present, and future. As we stand on the cusp of AI's next revolution, neuromorphic computing emerges as a beacon, guiding the way forward. The journey is just beginning, and the possibilities are boundless.