Neuromorphic Artificial Intelligence (AI)
With hundreds of millions of dollars flowing this year into silicon developments for training and running artificial intelligence (AI) deep neural networks (DNNs) via Nvidia, Intel, Nervana, Qualcomm, Graphcore, SambaNova, ARM and dozens of others, it is worthwhile asking what is left to be done. Won't silicon AI follow the same course as GPUs and become more and more tailored to efficiently compute industrial AI?
This tutorial addresses this question from the context of neuromorphic engineering, which takes its inspiration from the brain’s organizing principles. What have these principles of using sparsity, local memory, time, and physics brought to the table? I will describe recent developments of neuromorphic silicon from IBM, Intel, Zurich, Stanford, and others. I will also compare these with upcoming industrial AI accelerators, and then show that principles of sparsity and local memory reuse can bring immediate benefit to both convolutional and recurrent DNNs implemented in synchronous logic without requiring a new memory hierarchy. Finally, I will relate these ideas to event sensors, which our group has specialized in developing. I plan to include a live demonstration of some of these ideas.
Tobi Delbruck (M’99–SM’06–F’13) received a Ph.D. degree from Caltech in 1993 as a student of Carver Mead. He was in the first group of students for the newly founded Computation and Neural Systems program started by John Hopfield. He is currently a professor of physics and electrical engineering at ETH Zurich in the Institute of Neuroinformatics, University of Zurich and ETH Zurich, Switzerland, where he has been since 1998. The Sensors Group that he co-organizes with Dr. Shih-Chii Liu focuses on neuromorphic event-based sensors, sensory processing, and efficient deep neural network hardware architectures. He co-organizes the Telluride Neuromorphic Cognition Engineering summer workshop and has organized the live demonstration sessions at ISCAS and NIPS. Delbruck is past Chair of the IEEE CAS Sensory Systems Technical Committee. He worked on electronic imaging at Arithmos, Synaptics, National Semiconductor, and Foveon and has founded 4 spin-off companies, including inilabs.com, a community-oriented organization that has distributed R&D prototype neuromorphic sensors to more than a hundred organizations around the world. He has been awarded 9 IEEE awards.