The transistor celebrated its 75${}^\text{th}$ birthday in 2022. The continued scaling of the transistor defined by Moore's Law continues, albeit at a slower pace. Meanwhile, computing demands and energy consumption required by modern artificial intelligence (AI) algorithms have skyrocketed. As an alternative to scaling transistors for general-purpose computing, the integration of transistors with unconventional technologies has emerged as a promising path for domain-specific computing. In this article, we provide a full-stack review of probabilistic computing with p-bits as a representative example of the energy-efficient and domain-specific computing movement. We argue that p-bits could be used to build energy-efficient probabilistic systems, tailored for probabilistic algorithms and applications. From hardware, architecture, and algorithmic perspectives, we outline the main applications of probabilistic computers ranging from probabilistic machine learning and AI to combinatorial optimization and quantum simulation. Combining emerging nanodevices with the existing CMOS ecosystem will lead to probabilistic computers with orders of magnitude improvements in energy efficiency and probabilistic sampling, potentially unlocking previously unexplored regimes for powerful probabilistic algorithms.
Spin Vector Control for Heisenberg-Inspired Probabilistic Computing
Purdue University（普渡大学）；Birck Nanotechnology Center, Purdue University（普渡大学比尔克纳米技术中心）；University of California, Santa Barbara（加州大学圣塔芭芭拉分校）；King Fahd University of Petroleum & Minerals (KFUPM)（法赫德国王石油与矿产大学）
CMOS + stochastic nanomagnets: heterogeneous computers for probabilistic inference and learning
University of California Santa Barbara（加州大学圣巴巴拉分校）；Tohoku University（东北大学）；Research Institute of Electrical Communication, Tohoku University（东北大学电气通信研究所）；Graduate School of Engineering, Tohoku University（东北大学工学研究科）
Probabilistic Computing Optimization of Complex Spin-Glass Topologies
Quantum Dice Ltd（量子骰子公司）；Mathematical Institute, University of Oxford（牛津大学数学研究所）；Center for Computational Quantum Physics, Flatiron Institute（平顿研究所计算量子物理中心）；Department of Physics, University of Oxford（牛津大学物理系）
