IBM Advances Neuromorphic Computing for Deep Learning

Deep learning efforts today are run on standard computer hardware using convolutional neural networks. Indeed the approach has proven powerful by pioneers such as Google and Microsoft. In contrast neuromorphic computing, whose spiking neuron architecture more closely mimics human brain function, has generated less enthusiasm in the deep learning community. Now, work by IBM using its TrueNorth chip as a test case may bring deep learning to neuromorphic architectures. Writing in the Proceedings of the National Academy of Science (PNAS) in August (Convolutional networks for fast, energy-efficient neuromorphic computing), researchers from IBM Research report, "[We] demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that approach state-of-the-art classification accuracy across eight standard datasets encompassing vision and speech, perform inference while preserving the hardware's underlying energy-efficiency and high throughput."