  We present a new back propagation based training algorithm for discrete-time
spiking neural networks (SNN). Inspired by recent deep learning algorithms on
binarized neural networks, binary activation with a straight-through gradient
estimator is used to model the leaky integrate-fire spiking neuron, overcoming
the difficulty in training SNNs using back propagation. Two SNN training
algorithms are proposed: (1) SNN with discontinuous integration, which is
suitable for rate-coded input spikes, and (2) SNN with continuous integration,
which is more general and can handle input spikes with temporal information.
Neuromorphic hardware designed in 40nm CMOS exploits the spike sparsity and
demonstrates high classification accuracy (>98% on MNIST) and low energy
(48.4-773 nJ/image).
