Neuromorphic scaling advantages for energy-efficient random walk computations

Smith, J. Darby; Hill, Aaron J.; Reeder, Leah E.; Franke, Brian C.; Lehoucq, Richard B.; Parekh, Ojas; Severa, William; Aimone, James B.

doi:10.1038/s41928-021-00705-7

Title: Neuromorphic scaling advantages for energy-efficient random walk computations

Journal Article · Mon Feb 14 00:00:00 EST 2022 · Nature Electronics

DOI:https://doi.org/10.1038/s41928-021-00705-7· OSTI ID:1845387

^[1]; Hill, Aaron J. ^[1]; Reeder, Leah E. ^[1]; Franke, Brian C. ^[1]; Lehoucq, Richard B. ^[1]; Parekh, Ojas ^[1]; Severa, William ^[1];

^[1]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Neural Exploration and Research Lab.

Neuromorphic computing, which aims to replicate the computational structure and architecture of the brain in synthetic hardware, has typically focused on artificial intelligence applications. What is less explored is whether such brain-inspired hardware can provide value beyond cognitive tasks. Here we show that the high degree of parallelism and configurability of spiking neuromorphic architectures makes them well suited to implement random walks via discrete-time Markov chains. Overall, these random walks are useful in Monte Carlo methods, which represent a fundamental computational tool for solving a wide range of numerical computing tasks. Using IBM’s TrueNorth and Intel’s Loihi neuromorphic computing platforms, we show that our neuromorphic computing algorithm for generating random walk approximations of diffusion offers advantages in energy-efficient computation compared with conventional approaches. We also show that our neuromorphic computing algorithm can be extended to more sophisticated jump-diffusion processes that are useful in a range of applications, including financial economics, particle physics and machine learning.

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Research Organization:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA), Office of Defense Programs (DP)

Grant/Contract Number:: NA0003525

OSTI ID:: 1845387

Report Number(s):: SAND2022-1227J; 703214

Journal Information:: Nature Electronics, Vol. 5, Issue 2; ISSN 2520-1131

Publisher:: Springer NatureCopyright Statement

Country of Publication:: United States

Language:: English

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