Exploiting Omega Network and Inexact Accumulative Parallel Counter to Enhance Energy Efficiency in Stochastic Computing

Stochastic computing (SC) has garnered a great interest due to its energy efficiency and robustness against external noise, yet a long latency on stochastic computations and considerable overheads caused by conversions between binary numbers and stochastic numbers persist as notable challenges. This paper introduces a novel parallel random number generator (RNG) and accumulative parallel counters (APCs) to address both challenges. In particular, we propose a new parallel RNG design based on Omega network to bolster the randomness of generated numbers, thereby enhancing accuracy and reducing latency. Additionally, we introduce a novel APC design technique leveraging approximate 4-2 compressors to improve hardware efficiency while preserving the accuracy of SC computations. When implemented using a 65-nm CMOS technology, our proposed SC architecture outperforms other SC alternatives in terms of both hardware efficiency and computation accuracy. Specifically, our APC designs exhibit substantial enhancements of up to 30.1×, 26.6×, 5.9×, and 151× in area, power, delay, and energy, respectively, compared to traditional APCs. Also, we validate the efficacy of the proposed SC design through an image processing application, demonstrating superior processing quality alongside significantly enhanced hardware efficiency.