Introduction to Dynamic Stochastic Computing
Siting Liu, Warren J. Gross, Jie Han
Abstract
Stochastic computing (SC) is an old but reviving computing paradigm for its simple data path that can perform various arithmetic operations. It allows for low power implementation, which would otherwise be complex using the conventional positional binary coding. In SC, a number is encoded by a random bit stream of '0's and '1's with an equal weight for every bit. However, a long bit stream is usually required to achieve a high accuracy. This requirement inevitably incurs a long latency and high energy consumption in an SC system. In this article, we present a new type of stochastic computing that uses dynamically variable bit streams, which is, therefore, referred to as dynamic stochastic computing (DSC). In DSC, a random bit is used to encode a single value from a digital signal. A sequence of such random bits is referred to as a dynamic stochastic sequence. Using a stochastic integrator, DSC is well suited for implementing accumulation-based iterative algorithms such as numerical integration and gradient descent. The underlying mathematical models are formulated for functional analysis and error estimation. A DSC system features a higher energy efficiency than conventional computing using a fixed-point representation with a power consumption as low as conventional SC. It is potentially useful in a broad spectrum of applications including signal processing, numerical integration and machine learning.