Energy-Performance Scalability Analysis of a Novel Quasi-Stochastic Computing Approach

Abstract

Stochastic computing (SC) is an emerging low-cost computation paradigm for efficient approximation. It processes data in forms of probabilities and offers excellent progressive accuracy. Since SC’s accuracy heavily depends on the stochastic bitstream length, generating acceptable approximate results while minimizing the bitstream length is one of the major challenges in SC, as energy consumption tends to linearly increase with bitstream length. To address this issue, a novel energy-performance scalable approach based on quasi-stochastic number generators is proposed and validated in this work. Compared to conventional approaches, the proposed methodology utilizes a novel algorithm to estimate the computation time based on the accuracy. The proposed methodology is tested and verified on a stochastic edge detection circuit to showcase its viability. Results prove that the proposed approach offers a 12–60% reduction in execution time and a 12–78% decrease in the energy consumption relative to the conventional counterpart. This excellent scalability between energy and performance could be potentially beneficial to certain application domains such as image processing and machine learning, where power and time-efficient approximation is desired.