Paper Title: Making Computing More Brain-Like

Paper Abstract: Despite decades of progress in semiconductor scaling, computer architecture, and artificial intelligence, our computing technology today still lags biological brains in many respects. 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. For example, chips like Intel’s Loihi are for the first time able to demonstrate compelling quantitative gains over a range of workloads—from sensory perception to data efficient learning to combinatorial optimization. This talk will survey recent developments in this endeavor to re-think computing from transistors to software, as informed by biological principles. It will also outline the remaining research challenges, and will present a roadmap for progress spanning device-level innovation through software.

Paper Author: Mike Davies, Director, Neuromorphic Computing Lab, Intel

Author Bio: Mike Davies is Director of Intel’s Neuromorphic Computing Lab. Since 2014 he has been researching neuromorphic architectures, algorithms, software, and systems, and has fabricated several neuromorphic chip prototypes to date, including the Loihi series. He was a founding employee of Fulcrum Microsystems and Director of its silicon engineering group until Intel’s acquisition of Fulcrum in 2011. He led the development of four generations of low latency, highly integrated Ethernet switches using Fulcrum’s proprietary asynchronous design methodology. He received B.S. and M.S. degrees from Caltech in 1998 and 2000, respectively.