Efficient Spintronic Stochastic In-Sensor Computing Modules

Abstract

In-sensor processing (ISP) is used in various fields, including autonomous driving automotive systems, wearable healthcare devices, environmental monitoring, wireless sensor networks, smart cities, and industrial control systems. One of the main features of most of these applications is energy and hardware restriction. This restriction amplifies the importance of using high-performance and low-energy consumption computing systems and methods. This paper proposes efficient spintronic stochastic computing modules for in-sensor computing systems to address this challenge. The proposed design utilized the stochastic behavior of the magnetic tunnel junction in the subcritical switching regime to generate a probability-adjustable random stream. Probability-adjustable random stream generation is the most important step in any stochastic system because it determines the quality of the final output. The simulation results indicate that the proposed design offers almost the same peak signal-to-noise ratio and structural similarity while occupying at least 62% lower area and consuming 60% lower energy. These advantages made the proposed design a good choice for ISP applications with limited energy.