Probabilistic Neural Computing with Stochastic Devices.

Autor: Misra S; Microsystems Engineering, Science and Applications, Sandia National Laboratories, Albuquerque, NM, 87123, USA., Bland LC; Department of Physics, Temple University, Philadelphia, PA, 19122-1801, USA., Cardwell SG; Neural Exploration and Research Laboratory, Sandia National Laboratories, Albuquerque, NM, 87123, USA., Incorvia JAC; Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, 78712, USA., James CD; Microsystems Engineering, Science and Applications, Sandia National Laboratories, Albuquerque, NM, 87123, USA., Kent AD; Department of Physics, New York University, New York, NY, 10003, USA., Schuman CD; Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, 37996, USA., Smith JD; Neural Exploration and Research Laboratory, Sandia National Laboratories, Albuquerque, NM, 87123, USA., Aimone JB; Neural Exploration and Research Laboratory, Sandia National Laboratories, Albuquerque, NM, 87123, USA.
Jazyk: angličtina
Zdroj: Advanced materials (Deerfield Beach, Fla.) [Adv Mater] 2023 Sep; Vol. 35 (37), pp. e2204569. Date of Electronic Publication: 2022 Nov 17.
DOI: 10.1002/adma.202204569
Abstrakt: The brain has effectively proven a powerful inspiration for the development of computing architectures in which processing is tightly integrated with memory, communication is event-driven, and analog computation can be performed at scale. These neuromorphic systems increasingly show an ability to improve the efficiency and speed of scientific computing and artificial intelligence applications. Herein, it is proposed that the brain's ubiquitous stochasticity represents an additional source of inspiration for expanding the reach of neuromorphic computing to probabilistic applications. To date, many efforts exploring probabilistic computing have focused primarily on one scale of the microelectronics stack, such as implementing probabilistic algorithms on deterministic hardware or developing probabilistic devices and circuits with the expectation that they will be leveraged by eventual probabilistic architectures. A co-design vision is described by which large numbers of devices, such as magnetic tunnel junctions and tunnel diodes, can be operated in a stochastic regime and incorporated into a scalable neuromorphic architecture that can impact a number of probabilistic computing applications, such as Monte Carlo simulations and Bayesian neural networks. Finally, a framework is presented to categorize increasingly advanced hardware-based probabilistic computing technologies.
(© 2022 The Authors. Advanced Materials published by Wiley-VCH GmbH.)
Databáze: MEDLINE
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