Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing
This enables embedded, intelligent brain-inspired computing by merging deep learning with neuromorphic processors, though it is incremental in combining existing methods.
The authors tackled the challenge of implementing deep convolutional networks on neuromorphic hardware while maintaining energy efficiency, achieving state-of-the-art classification accuracy across 8 datasets and demonstrating inference speeds of 1200-2600 frames per second with power consumption of 25-275 mW.
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.