Abstract:Neuromorphic computing draws inspiration from the brain's structure to create energy-efficient hardware for running neural networks.  Pursuing this vision, we created the TrueNorth chip, which embodies 1 million neurons and 256 million configurable synapses in contemporary silicon technology, and runs using under 100 milliwatts.  Spiking neurons, low-precision synapses and constrained connectivity are key design factors in achieving chip efficiency, though they stand in contrast to today's conventional neural networks that use high precision neurons and synapses and have unrestricted connectivity.  Conventional networks are trained today using deep learning, a field developed independent of neuromorphic computing, and are able to achieve human-level performance on a broad spectrum of recognition tasks.  Until recently, it was unclear whether the constraints of energy-efficient neuromorphic computing were compatible with networks created through deep learning.  Taking on this challenge, we demonstrated that relatively minor modifications to deep learning methods allows for the creation of high performing networks that can run on the TrueNorth chip.  The approach was demonstrated on 8 standard datasets encompassing vision and speech, where near state-of-the-art performance was achieved while maintaining the hardware's underlying energy-efficiency to run at > 6000 frames / sec / watt.  In this talk, I will present an overview of the TrueNorth chip, our methods to train networks for this chip and a selection of performance results.