Accelerating Deterministic Stochastic Computing with Context-Aware Bit-stream Generator
Sina Asadi, M. Hassan Najafi · 2020
Deterministic approaches to stochastic computing were proposed recently to produce completely accurate results with stochastic logic. Real-valued numbers in the [0,1] interval are converted to unary or pseudo-random bit-streams and processed using the relatively prime bit-stream length, clock division, or rotation method. Fast converging deterministic methods based on low-discrepancy bit-streams were also introduced. Long latency is the main issue with all these deterministic methods. To process m n-bit precision numbers, bit-streams of 2(m*n) bits must be generated. In this work, we propose a context-aware bit-stream generator to improve the performance of the deterministic bit-stream processing systems. The proposed design reduces the processing time up to 86% for the cases that completely accurate results are desired. When the application can tolerate some small rates of inaccuracy orders of magnitude reduction in the latency are achievable.