Deep learning has been enabled by powerful hardware and its progress is gated by improvements in 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.