Power-Efficient Spiking Neural Networks

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Inspired by the neurons and synapses of human's brain, we focus on exploring the advantages and disadvantages of spiking neural networks (SNNs). Then scalable and energy-efficient SNNs have been proposed to use time (spikes) of the signal to process information. The spike is essentially a binary event, it is either 0 or 1. The main advantage of SNNs is that it can make the full use of time and space information. Using time as an additional input dimension, SNNs record valuable information in a sparse manner. The neuron in SNNs is in an active state only when receiving or generating a peak signal, which means that it is driven by events, so it can save energy. If there is no spike coming, the neuron will remain idle. In addition, the input value in SNN is 1 or 0, which also reduces the multiplication operation, to a less computation load. There are several ways to encode the input data of SNNS. When looking into ways to reduce the hardware cost of neural networks, stochastic computing (SC) becomes appealing due to the simple logic used for complex computation. Unlike the conventional binary encoding, SC operates on randomly generated binary sequences. Using random numbers to encode input data, the SNNs are called stochastic SNNs. In this project, we focus on the design, evaluation and implementation of stochastic SNNs. We will also explore the market opportunity and work toward the commercialization of the developed technology.