  On-chip edge intelligence has necessitated the exploration of algorithmic
techniques to reduce the compute requirements of current machine learning
frameworks. This work aims to bridge the recent algorithmic progress in
training Binary Neural Networks and Spiking Neural Networks - both of which are
driven by the same motivation and yet synergies between the two have not been
fully explored. We show that training Spiking Neural Networks in the extreme
quantization regime results in near full precision accuracies on large-scale
datasets like CIFAR-$100$ and ImageNet. An important implication of this work
is that Binary Spiking Neural Networks can be enabled by "In-Memory" hardware
accelerators catered for Binary Neural Networks without suffering any accuracy
degradation due to binarization. We utilize standard training techniques for
non-spiking networks to generate our spiking networks by conversion process and
also perform an extensive empirical analysis and explore simple design-time and
run-time optimization techniques for reducing inference latency of spiking
networks (both for binary and full-precision models) by an order of magnitude
over prior work.
