STRAUSS: Spectral Transform Use in Stochastic Circuit Synthesis

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.