The current resurgence of artificial intelligence is due to advances in deep learning. Systems based on deep learning now exceed human capability in speech recognition, object classification, and playing games like Go. Deep learning has been enabled by powerful, efficient computing hardware. The algorithms used have been around since the 1980s, but it has only been in the last decade - when powerful GPUs became available to train networks - that the technology has become practical. Advances in DL are now gated by hardware performance. This talk will review the current state of deep learning hardware and explore a number of directions to continue performance scaling in the absence of Moore’s Law. Topics discussed will include number representation, sparsity, memory organization, optimized circuits, and analog computation. Speaker(s): Bill Dally, Virtual: https://events.vtools.ieee.org/m/290553