Title: Energy-Efficient Hybrid Stochastic-Binary Neural Networks for Near-Sensor Computing,
Abstract: Recent advances in neural networks (NNs) exhibit unprecedented success at
transforming large, unstructured data streams into compact higher-level
semantic information for tasks such as handwriting recognition, image
classification, and speech recognition. Ideally, systems would employ
near-sensor computation to execute these tasks at sensor endpoints to maximize
data reduction and minimize data movement. However, near- sensor computing
presents its own set of challenges such as operating power constraints, energy
budgets, and communication bandwidth capacities. In this paper, we propose a
stochastic- binary hybrid design which splits the computation between the
stochastic and binary domains for near-sensor NN applications. In addition, our
design uses a new stochastic adder and multiplier that are significantly more
accurate than existing adders and multipliers. We also show that retraining the
binary portion of the NN computation can compensate for precision losses
introduced by shorter stochastic bit-streams, allowing faster run times at
minimal accuracy losses. Our evaluation shows that our hybrid stochastic-binary
design can achieve 9.8x energy efficiency savings, and application-level
accuracies within 0.05% compared to conventional all-binary designs.