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In this work, we propose stochastic Binary Spiking Neural Network (sBSNN)composed of stochastic spiking neurons and binary synapses (stochastic onlyduring training) that computes probabilistically with one-bit precision forpower-efficient and memory-compressed neuromorphic computing. We present anenergy-efficient implementation of the proposed sBSNN using 'stochastic bit' asthe core computational primitive to realize the stochastic neurons andsynapses, which are fabricated in 90nm CMOS process, to achieve efficienton-chip training and inference for image recognition tasks. The measured datashows that the 'stochastic bit' can be programmed to mimic spiking neurons, andstochastic Spike Timing Dependent Plasticity (or sSTDP) rule for training thebinary synaptic weights without expensive random number generators. Our resultsindicate that the proposed sBSNN realization offers possibility of up to 32xneuronal and synaptic memory compression compared to full precision (32-bit)SNN and energy efficiency of 89.49 TOPS/Watt for two-layer fully-connected SNN.

sBSNN: Stochastic-Bits Enabled Binary Spiking Neural Network with On-Chip Learning for Energy Efficient Neuromorphic Computing at the Edge