Optimizing stochastic circuits for accuracy-energy tradeoffs

Stochastic computing (SC) acts on data encoded by bit-streams, and is an attractive, low-cost and error-tolerant alternative to conventional binary circuits in some important applications such as image processing and communications. We study the use of energy reduction techniques such as voltage or frequency scaling in SC circuits. We show that due to their inherent error-tolerance, SC circuits operate satisfactorily without significant accuracy loss even with aggressive scaling that improves their energy efficiency by orders of magnitude. To find the minimum-energy operating point of an SC circuit, we propose a Markov chain model that allows us to quickly explore the space of operating points. We also investigate opportunities to optimize SC circuits under such aggressive scaling. We find that logical and physical design techniques can be used to significantly expand the already powerful accuracy-energy tradeoff possibilities in SC circuits. Our simulation results show that our optimized SC circuits can tolerate aggressive voltage scaling with no significant SNR degradation after 40% supply voltage reduction (1V to 0.6V), leading to 66% energy saving (20.7pJ to 6.9pJ). Similarly, a 100% frequency boosting (400ps to 200ps) of the optimized circuits leads to no significant SNR degradation for several representative circuits.