Integrating P-bits in MTJs: A Bridge to Efficient Stochastic Computing

Probabilistic computing has emerged as a promising paradigm to tackle computational challenges in artificial intelligence (AI), machine learning (ML), and optimization tasks. Unlike deterministic computing, it leverages controlled randomness to enhance efficiency and scalability. This paper explores the integration of probabilistic bits (p-bits) within magnetic tunnel junction (MTJ) devices, utilizing their inherent stochastic switching behavior to implement probabilistic computing architectures. It demonstrates how MTJs enable energy-efficient, hardware-compatible solutions by eliminating the need for complex pseudo-random number generators. A crossbar-based p-bit array is proposed, incorporating a digital-to-analog converter (DAC) and a sense amplifier (SA) to facilitate probabilistic operations. This work highlights MTJ-based probabilistic computing as a viable alternative for future low-power, high-performance AI accelerators.