A full-stack view of probabilistic computing with p-bits: devices, architectures and algorithms

• URL: http://arxiv.org/abs/2302.06457v3
• Date: Thu, 16 Mar 2023 05:26:46 GMT
• Title: A full-stack view of probabilistic computing with p-bits: devices, architectures and algorithms
• Authors: Shuvro Chowdhury, Andrea Grimaldi, Navid Anjum Aadit, Shaila Niazi, Masoud Mohseni, Shun Kanai, Hideo Ohno, Shunsuke Fukami, Luke Theogarajan, Giovanni Finocchio, Supriyo Datta and Kerem Y. Camsari
• Abstract summary: We provide a full-stack review of probabilistic computing with p-bits. We argue that p-bits could be used to build energy-efficient probabilistic systems. We outline the main applications of probabilistic computers ranging from machine learning to AI.
• Score: 0.014319921806060482
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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