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Deep Spiking Convolutional Neural Networks for Programmable Neuro-synaptic System

Binary neural networks (BNNs) - neural networks with binary {-1, +1} weights and activations at run-time, have attracted much attention in recent years, which drastically reduce resource requirement and power consumption with tolerable accuracy loss. While, spiking neural networks (SNNs) are designed as a brain-inspired computing model, which can be deployed on large-scale distributed and event-based neuromorphic hardware efficiently. To enable a conversion of above two, we develop a variant of BNNs which employ binary-valued {0, +1} activations and ternary-valued {-1, 0, +1} weights and achieve start-of-the-art classification accuracy across two standard vision datasets, even better than the previous proposals using BNNs or SNNs. Furtherly, we propose an effective configuration method for network deployment on our designed programmable neuro-synaptic system with only one timestep and milliwatt level power consumption.