Neuromorphic scaling advantages for energy-efficient random walk computation

Bibliographic Details
Title: Neuromorphic scaling advantages for energy-efficient random walk computation
Authors: Smith, J. Darby, Hill, Aaron J., Reeder, Leah E., Franke, Brian C., Lehoucq, Richard B., Parekh, Ojas, Severa, William, Aimone, James B.
Source: Nature Electronics 2022
Publication Year: 2021
Collection: Computer Science
Mathematics
Subject Terms: Computer Science - Neural and Evolutionary Computing, Computer Science - Distributed, Parallel, and Cluster Computing, Mathematics - Numerical Analysis, Mathematics - Probability
More Details: Computing stands to be radically improved by neuromorphic computing (NMC) approaches inspired by the brain's incredible efficiency and capabilities. Most NMC research, which aims to replicate the brain's computational structure and architecture in man-made hardware, has focused on artificial intelligence; however, less explored is whether this brain-inspired hardware can provide value beyond cognitive tasks. We demonstrate that high-degree parallelism and configurability of spiking neuromorphic architectures makes them well-suited to implement random walks via discrete time Markov chains. Such random walks are useful in Monte Carlo methods, which represent a fundamental computational tool for solving a wide range of numerical computing tasks. Additionally, we show how the mathematical basis for a probabilistic solution involving a class of stochastic differential equations can leverage those simulations to provide solutions for a range of broadly applicable computational tasks. Despite being in an early development stage, we find that NMC platforms, at a sufficient scale, can drastically reduce the energy demands of high-performance computing (HPC) platforms.
Comment: Paper, figures, supplement
Document Type: Working Paper
DOI: 10.1038/s41928-021-00705-7
Access URL: http://arxiv.org/abs/2107.13057
Accession Number: edsarx.2107.13057
Database: arXiv
More Details
DOI:10.1038/s41928-021-00705-7