Probabilistic Computing With p-Bits: Optimization, Machine Learning and Quantum Simulation

University of California, Santa Barbara

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The slowing down of Moore’s Law growth has coincided with escalating computational demands from machine learning and artificial intelligence. An emerging trend in computing involves building physics-inspired computers that leverage the intrinsic properties of physical systems for specific domains of applications. Probabilistic computing with probabilistic bits (p-bits) has emerged as a promising candidate in this area, offering an energy-efficient approach to probabilistic algorithms and applications [1]-[4].

Several implementations of p-bits, ranging from standard complementary metal oxide semiconductor (CMOS) technology to nanodevices, have been demonstrated. Among these, the most promising p-bits appear to be based on stochastic magnetic tunnel junctions (sMTJs) [2]. Such sMTJs harness the natural randomness in low-barrier nanomagnets to create energy-efficient and fast fluctuations, up to gigahertz frequencies [4]. In this talk, I will discuss how magnetic p-bits can be combined with conventional CMOS to create hybrid probabilistic-classical computers for various applications. I will provide recent examples of how p-bits are naturally applicable to combinatorial optimization, such as solving the Boolean satisfiability problem [3], energy-based generative machine learning models like deep Boltzmann machines, and quantum simulation for investigating many-body quantum systems. Through experimentally informed projections for scaled p-bit computers using sMTJs, I will demonstrate how physics-inspired probabilistic computing can lead to graphics-processing-unit-like success stories for a sustainable future in computing.

[1] S. Chowdhury, A. Grimaldi, N. A. Aadit, S. Niazi, M. Mohseni, S. Kanai, H. Ohno, S. Fukami, L. Theogarajan, G. Finocchio, S. Datta, K. Y. Camsari, “A Full-Stack View of Probabilistic Computing with p-Bits: Devices, Architectures and Algorithms,” IEEE J. Expl. Solid-State Comp. Dev. Cir. 9, 1-11 (2023).

[2] W. A. Borders, A. Z. Pervaiz, S. Fukami, K. Y. Camsari, H. Ohno, S. Datta, “Integer Factorization Using Stochastic Magnetic Tunnel Junctions,” Nature 573, 390-393 (2019).

[3] N. A. Aadit, A. Grimaldi, M. Carpentieri, L. Theogarajan, J. M. Martinis, G. Finocchio, K. Y. Camsari, “Massively Parallel Probabilistic Computing with Sparse Ising Machines,” Nature Electronics 5, 460–468 (2022).

[4] N. S. Singh, S. Niazi, S. Chowdhury, K. Selcuk, H. Kaneko, K. Kobayashi, S. Kanai, H. Ohno, S. Fukami, K. Y. Camsari, “Hardware Demonstration of Feedforward Stochastic Neural Networks with Fast MTJ-Based p-Bits,” IEEE Int. Electron Dev. Meeting (2023).