Learning How to Learn: Neuromorphic AI Inference at the Edge
Lays out the structural limits facing conventional AI silicon — the von Neumann bottleneck, the slowing of Moore’s Law, and the end of Dennard scaling — compounded by the bandwidth, latency, security, and energy costs of sending raw data to the cloud. Explains how neuromorphic architectures answer these constraints by keeping inference, and on-device learning, local to the edge device.
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