Exploiting Randomness in Stochastic Computing

Stochastic computing (SC) computes with randomized bit-streams using standard logic circuits. Its defining features are low power, small area, and high fault tolerance; its drawbacks are long run times and inaccuracies due to its inherently random behavior. Consequently, much previous work has focused on improving SC performance by introducing non-random or deterministic data formats and components, often at considerable cost. However, as this paper shows, taking advantage of, or even adding to, a stochastic circuit's randomness can play a major positive role in applications like neural networks (NNs). The amount of such randomness, must however, be carefully controlled to achieve a beneficial effect without corrupting an application's functionality. The paper first discusses the use of mean square deviation (MSD) as a metric for randomness in SC. It then describes a low-cost element to control the MSD levels of stochastic signals. Finally, it examines two applications where SC can provide performance-enhancing randomness at very low cost, while retaining all the other benefits of SC. Specifically, it is shown how to improve the visual quality of black-and-white images via stochastic dithering, a technique that leverages randomness to enhance image details. Further, the paper demonstrates how the randomness of an SC-based layer makes an NN more resilient against adversarial attacks than an NN realized entirely by conventional, non-stochastic designs.