STRAUSS: Spectral Transform Use in Stochastic Circuit Synthesis

Abstract

1 Stochastic computing (SC) is an approximate computing technique that processes data in the form of long pseudorandom bit-streams which can be interpreted as probabilities. Its key advantages are low-complexity hardware and high-error tolerance. SC has recently been finding application in several important areas, including image processing, artificial neural networks, and low-density parity check decoding. Despite a long history, SC still lacks a comprehensive design methodology, so existing designs tend to be either ad hoc or based on specialized design methods. In this paper, we demonstrate a fundamental relation between stochastic circuits and spectral transforms. Based on this, we propose a general, transform-based approach to the analysis and synthesis of SC circuits. We implemented this approach in a program spectral transform use in stochastic circuit synthesis (STRAUSS), which also includes a method of optimizing stochastic number-generation circuitry. Finally, we show that the area cost of the circuits generated by STRAUSS is significantly smaller than that of previous work.1Parts of this paper are based on 'A spectral transform approach to stochastic circuits,' which was presented at the International Conference on Computer Design, Oct. 2012 [3].