Stochastic Computing Can Improve Upon Digital Spiking Neural Networks

With the surge in popularity of machine learning algorithms, research has turned towards exploring novel computing architectures in order to increase performance while limiting power consumption. Inspired by their biological counterparts, digital spiking neural networks have emerged as energy efficient alternatives to conventional hardware implementations, yet remain largely incompatible with cutting edge learning methods. Representing information with single-bit binary pulse trains, the behaviour of spiking neural networks exhibit many interesting analogues to the existing field of stochastic computing. In this paper, we not only illustrate the parallels between digital spiking neural networks and stochastic computing, but we also demonstrate that many computing elements in modern spiking hardware are, in fact, implementations of stochastic circuits. In addition, we show that stochastic computing design techniques can be leveraged in order to address shortcomings in current spiking neural network architectures.