Asynchronous Stochastic Computing

Asynchronous Stochastic Computing (ASC) leverages Synchronous Stochastic Computing (SSC) advantages and addresses its drawbacks. In SSC a multiplier is a single AND gate, saving similar to 90% of power and area compared with a typical 8bit binary multiplier. The key for SSC power-area efficiency comes from mapping numbers to streams of 1s and 0s. Despite the power-area efficiency, SSC drawbacks such as long latency, costly clock distribution network (CDN), and expensive stream generation, causes the energy consumption to grow prohibitively large. In this work, we introduce the foundations for ASC using Continuous-time-Markov-chains, and analyze the computing error due to random fluctuations. In ASC data is mapped to asynchronous-continuous-time streams, which yields two advantages over the synchronous counterpart: (1) CDN elimination, and (2) better accuracy performance. We compare ASC with SSC for three applications: (1) multiplication, (2) an image processing algorithm: gamma-correction, and (3) a single-layer of a fully-connected artificial-neural-network (ANN) using a FinFET1X technology. Our Matlab, Spice-level simulations and post-place&route (P&R) reports demonstrate that ASC yields savings of 10%-55%, 33%-44%, and 50% in latency, power, and energy respectively. These savings make ASC a good candidate to address the ultra-low-power requirements of machine learning for the IoT.