A spectral transform approach to stochastic circuits
Armin Alaghi, John P. Hayes · 2012
Stochastic computing (SC) processes data in the form of long pseudo-random bit-streams denoting probabilities. Its key advantages are simple computational elements and high soft-error tolerance. Recent technology developments have revealed important new SC applications such as image processing and LDPC decoding. Despite its long history, SC still lacks a comprehensive design methodology; existing methods tend to be ad hoc and limited to a few arithmetic functions. We demonstrate a fundamental relation between stochastic circuits and spectral transforms. Based on this, we propose a transform approach to the analysis and synthesis of SC circuits. We illustrate the approach for a variety of basic combinational SC design problems, and show that the area cost associated with stochastic number generation can be significantly reduced.