Some more exposure for Brainchip.....
Not all edges are created equal
What makes the smart edge murky is the definition of edge, which means different things to different people, Nandan Nayampally, CMO at BrainChip, said in an interview with Fierce Electronics. He was previously at ARM for more than 15 years when the edge was viewed as primarily sensor driven. “That's how IoT kind of cropped up,” he said. “IoT is a sensor plus connectivity plus processing.” While a Dell or an Intel might think of the smart edge as another giant box that’s now smaller, the right starting point to him is IoT with AI.
AI on the edge is a step forward from a sensor just doing one function, with devices now having more storage, memory, and processing power. Nayampally said this battle between cloud and the edge has been going on a for a while, going back to the days of a terminal connected to mainframe before the move to a client/server model. “What you realize is however much we think that latency to cloud or connectivity to cloud is guaranteed, and the bandwidth assured, it's never going to be the case,” he said. “You need that intelligence and computational power at the edge.”
BrainChip's Akida processor can learn at the edge to address security and privacy while limiting network congestion. (BrainChip)
Having the smarts at the edge is beneficial for preventative maintenance in factories and patient monitoring, Nayampally said, both in terms of latency and privacy. “Anytime you send raw data or sensitive data out, you are obviously going to have challenges.” Privacy and security have become especially important to the general public, he added. BrainChip was started with the idea that edge computing was necessary and that any approach to AI at the edge had to be different from the cloud. “The cloud kind of assumes almost infinite resources and infinite compute.”
While compute resources at the edge are rather finite, more AI is possible due to advances with low power hardware including memory and systems on chip (SoC), which means not all training and machine learning need be shipped back to the cloud. Nayampally said it’s a matter of scaling, with neuromorphic computing offering inspiration for how to low power intelligence at the edge. “Let's try to emulate the efficiency of it and start from there.”
Machine learning will increasingly happen at the edge both because of inherent capability but also out of necessity. Nayampally said some applications that require a real-time response can’t afford the delay between the edge and the cloud, or the power. “Any time you use radio and connectivity, especially to cloud, that burns a lot of power,” he said. “Radios are the most expensive parts of devices.” Smaller, more cost-effective devices may not be able to afford to have connectivity and need to do more compute locally.
Nayampally said the neuromorphic nature of BrainChip’s Akida platform allows it to learn at the edge, which also addresses security and privacy and reduces network congestion – today’s autonomous vehicles can generate a terabyte of data per day, he noted, so it makes sense to be selective about how much data needs to travel the network.
For the smart edge, simple is better and BrainChip’s processor does that from a computational standpoint, as well as from a development and deployment standpoint, Nayampally said. “It's almost like a self-contained processor.” It is neuromorphic and event driven, so it only processes data when needed, and only communicates when needed, he said.
The one to talk with shareholders is the shareholder relations person, NOT Sean. Give the man a break and let him do the job which is MOST important for us. From everything I am seeing and hearing he is doing a great job despite limitations to what he can officially reveal. Keep it up Sean, and well done!
