  Deep networks are now able to achieve human-level performance on a broad
spectrum of recognition tasks. Independently, neuromorphic computing has now
demonstrated unprecedented energy-efficiency through a new chip architecture
based on spiking neurons, low precision synapses, and a scalable communication
network. Here, we demonstrate that neuromorphic computing, despite its novel
architectural primitives, can implement deep convolution networks that i)
approach state-of-the-art classification accuracy across 8 standard datasets,
encompassing vision and speech, ii) perform inference while preserving the
hardware's underlying energy-efficiency and high throughput, running on the
aforementioned datasets at between 1200 and 2600 frames per second and using
between 25 and 275 mW (effectively > 6000 frames / sec / W) and iii) can be
specified and trained using backpropagation with the same ease-of-use as
contemporary deep learning. For the first time, the algorithmic power of deep
learning can be merged with the efficiency of neuromorphic processors, bringing
the promise of embedded, intelligent, brain-inspired computing one step closer.

Neural and Evolutionary Computing