Spiking neural network (SNN) is interesting both theoretically and
practically because of its strong bio-inspiration nature and potentially
outstanding energy efficiency. Unfortunately, its development has fallen far
behind the conventional deep neural network (DNN), mainly because of difficult
training and lack of widely accepted hardware experiment platforms. In this
paper, we show that a deep temporal-coded SNN can be trained easily and
directly over the benchmark datasets CIFAR10 and ImageNet, with testing
accuracy within 1% of the DNN of equivalent size and architecture. Training
becomes similar to DNN thanks to the closed-form solution to the spiking
waveform dynamics. Considering that SNNs should be implemented in practical
neuromorphic hardwares, we train the deep SNN with weights quantized to 8, 4, 2
bits and with weights perturbed by random noise to demonstrate its robustness
in practical applications. In addition, we develop a phase-domain signal
processing circuit schematic to implement our spiking neuron with 90% gain of
energy efficiency over existing work. This paper demonstrates that the
temporal-coded deep SNN is feasible for applications with high performance and
high energy efficient