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：晶体管理论于2022年迎来其75岁诞辰。由摩尔定律定义的晶体管持续缩放仍在继续，尽管速度有所放缓。与此同时，现代人工智能算法对计算能力与能耗的需求呈指数级增长。作为通用计算中晶体管缩放技术的替代方案，将晶体管与非常规技术相融合已成为领域专用计算领域颇具前景的发展路径。本文以能效优先的领域专用计算运动典型范例——基于p-bit的概率计算为切入点，提供全栈视角的综述。我们认为p-bit可用于构建专为概率算法与应用设计的能效型概率系统。从硬件、架构与算法三个维度，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、人工智能、组合优化及量子模拟等领域。通过将新兴纳米器件与现有CMOS生态系统相结合，概率计算机有望在能效与概率采样方面实现数量级提升，进而解锁此前难以触及的强大概率算法应用新前沿。