A Full-Stack View of Probabilistic Computing With p-Bits: Devices, Architectures, and Algorithms
A Full-Stack View of Probabilistic Computing With p-Bits: Devices, Architectures, and Algorithms
复制标题
DOI：
10.1109/jxcdc.2023.3256981
复制
发表时间：
2023-02
影响因子：
2.4
通讯作者：
S. Chowdhury;Andrea Grimaldi;Navid Anjum Aadit;Shaila Niazi;M. Mohseni;S. Kanai;Hideo Ohno;S. F
中科院分区：
文献类型：
--
作者：
S. Chowdhury;Andrea Grimaldi;Navid Anjum Aadit;Shaila Niazi;M. Mohseni;S. Kanai;Hideo Ohno;S. F
晶体管在2022年迎来了它的75岁生日。由摩尔定律所定义的晶体管持续缩小仍在继续，尽管速度有所放缓。与此同时，现代人工智能（AI）算法所需的计算需求和能耗急剧上升。作为通用计算中晶体管缩小的一种替代方案，将晶体管与非传统技术相结合已成为特定领域计算的一条有前途的途径。在本文中，我们以p位为例，对概率计算进行了全栈综述，它是节能和特定领域计算运动的一个代表性例子。我们认为，p位可用于构建节能的概率系统，为概率算法和应用量身定制。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，范围从概率机器学习（ML）和人工智能到组合优化和量子模拟。将新兴的纳米器件与现有的CMOS生态系统相结合，将使概率计算机在能效和概率采样方面有数量级的提高，有可能为强大的概率算法开启以前未探索的领域。
The transistor celebrated its 75th 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 (ML) 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.