Data Driven Optimizations for MTJ based Stochastic Computing

Abstract

Stochastic computing, a form of computation with probabilities, presents an alternative to conventional arithmetic units. Magnetic Tunnel Junctions (MTJs), which exhibit probabilistic switching, have been explored as Stochastic Number Generators (SNGs). We provide a perspective of the energy requirements of such an application and design an energy-efficient and data-sensitive MTJ-based SNG. We discuss its benefits when used for stochastic computations, illustrating with the help of a multiplier circuit, in terms of energy savings when compared to computing with the baseline MTJ-SNG.

2 pages, Accepted for poster presentation in the Workshop on Approximate Computing 2016, AC'16