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:晶体体管在2022年庆祝了75美元的生日。 摩尔法律定义的晶体管继续扩大, 尽管速度较慢。 与此同时,现代人工智能(AI)算法所需要的计算需求和能源消耗量飞速上升。 作为一般目的计算光晶体管规模的替代方法,晶体管与非常规技术的整合已成为特定领域计算的一个有希望的道路。 在文章中,我们提供了对概率计算的全面审查,P比特作为节能和特定领域计算运动的一个代表性例子。 我们争辩说,p比特可用于建立节能概率概率系统,专门设计用于概率性算法和应用。 从硬件、架构和算法的角度,我们概述了概率计算机的主要应用,从概率机器学习到调整优化和量量模拟。 将新兴纳米比特机与现有的CMOS生态系统结合,将导致稳定性计算机与能源效率和强度预测性强的精确度改进系统。</s>