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.

翻译：2022年，晶体管迎来了其诞生75周年。尽管速度有所放缓，但由摩尔定律定义的晶体管持续微缩仍在继续。与此同时，现代人工智能算法对计算能力和能耗的需求急剧攀升。作为通用计算中晶体管微缩的替代方案，将晶体管与非传统技术融合已成为领域专用计算领域一条前景广阔的路径。本文以p-bit作为能效型领域专用计算运动的代表性实例，对概率计算进行了全栈综述。我们认为，p-bit可用于构建针对概率算法与应用量身定制的能效型概率系统。从硬件、架构和算法三个层面，我们概述了概率计算机的主要应用场景，涵盖概率机器学习与人工智能、组合优化以及量子模拟。将新兴纳米器件与现有CMOS生态系统相结合，将催生出在能效和概率采样方面实现数量级提升的概率计算机，从而可能为强大的概率算法解锁此前未探索的全新领域。