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Spiking neural network (SNN) is interesting both theoretically andpractically because of its strong bio-inspiration nature and potentiallyoutstanding energy efficiency. Unfortunately, its development has fallen farbehind the conventional deep neural network (DNN), mainly because of difficulttraining and lack of widely accepted hardware experiment platforms. In thispaper, we show that a deep temporal-coded SNN can be trained easily anddirectly over the benchmark datasets CIFAR10 and ImageNet, with testingaccuracy within 1% of the DNN of equivalent size and architecture. Trainingbecomes similar to DNN thanks to the closed-form solution to the spikingwaveform dynamics. Considering that SNNs should be implemented in practicalneuromorphic hardwares, we train the deep SNN with weights quantized to 8, 4, 2bits and with weights perturbed by random noise to demonstrate its robustnessin practical applications. In addition, we develop a phase-domain signalprocessing circuit schematic to implement our spiking neuron with 90% gain ofenergy efficiency over existing work. This paper demonstrates that thetemporal-coded deep SNN is feasible for applications with high performance andhigh energy efficient.

Temporal-Coded Deep Spiking Neural Network with Easy Training and Robust Performance