Abstract: Despite decades of progress in semiconductor scaling, computer architecture, and artificial intelligence, in many respects our computing technology today still lags biological brains. While deep artificial neural networks have provided breakthroughs in AI, these gains come with heavy compute and data demands relative to their biological counterparts. Neuromorphic computing aims to narrow this gap by drawing inspiration from the form and function of biological neural circuits. The past several years have seen significant progress in neuromorphic computing research, with chips like Intel’s Loihi providing, for the first time, compelling quantitative results over a range of workloads — from sensory perception to data efficient learning to combinatorial optimization.

This talk surveys recent developments in this endeavor to re-think computing from transistors to software informed by biological principles. It previews a new class of chips that can autonomously process complex data streams, adapt, plan, behave, and learn in real time under power, data, and latency constraints.

Bio: Mike Davies is Director of Intel’s Neuromorphic Computing Lab. He received B.S. and M.S. degrees from Caltech.