Very long article by an Adjunct Professor at Stanford - including a couple of references to BrainChip (snipped below)
Nothing major/new but further invaluable industry validation nonetheless
“If you haven’t paid attention, nows the time”
Hundreds of billions in public and private capital is being invested in Artificial Intelligence (AI) and Machine Learning companies. The number of patents… | 28 comments on LinkedIn
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Spiking Neural Networks (SNN) is a completely different approach from Deep Neural Nets. A form of Neuromorphic computing it tries to emulate how a brain works. SNN neurons use simple counters and adders—no matrix multiply hardware is needed and power consumption is much lower. SNNs are good at unsupervised learning – e.g. detecting patterns in unlabeled data streams. Combined with their low power they’re a good fit for sensors at the edge. Examples: BrainChip, GrAI Matter, Innatera, Intel
Ultralow-Power AI Chips Target IoT Sensors – IoT devices require very simple neural networks and can run for years on a single battery. Example applications: Presence detection, wakeword detection, gunshot detection… Examples: Syntiant (NDP,)Innatera, BrainChip