A review of non-cognitive applications for neuromorphic computing

James B. Aimone; Prasanna Date; Gabriel A. Fonseca-Guerra; Kathleen E. Hamilton; Kyle Henke; Bill Kay; Garrett T. Kenyon; Shruti R. Kulkarni; Susan M. Mniszewski; Maryam Parsa; Sumedh R. Risbud; Catherine D. Schuman; William Severa; J. Darby Smith

doi:10.1088/2634-4386/ac889c

A review of non-cognitive applications for neuromorphic computing

James B. Aimone, Prasanna Date, Gabriel A. Fonseca-Guerra, Kathleen E. Hamilton, Kyle Henke, Bill Kay, Garrett T. Kenyon, Shruti R. Kulkarni, Susan M. Mniszewski, Maryam Parsa, Sumedh R. Risbud, Catherine D. Schuman, William Severa, J. Darby Smith

Research output: Contribution to journal › Review article › peer-review

26 Scopus citations

Abstract

Though neuromorphic computers have typically targeted applications in machine learning and neuroscience (‘cognitive’ applications), they have many computational characteristics that are attractive for a wide variety of computational problems. In this work, we review the current state-of-the-art for non-cognitive applications on neuromorphic computers, including simple computational kernels for composition, graph algorithms, constrained optimization, and signal processing. We discuss the advantages of using neuromorphic computers for these different applications, as well as the challenges that still remain. The ultimate goal of this work is to bring awareness to this class of problems for neuromorphic systems to the broader community, particularly to encourage further work in this area and to make sure that these applications are considered in the design of future neuromorphic systems.

Original language	English
Article number	032003
Journal	Neuromorphic Computing and Engineering
Volume	2
Issue number	3
DOIs	https://doi.org/10.1088/2634-4386/ac889c
State	Published - Sep 1 2022

Funding

This material is based upon work supported in part by the US Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Robinson Pino, program manager, under Contract Number DE-AC05-00OR22725. This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Robinson Pino, program manager, under Award Number DE-SC0022566. This material is based upon work supported in part by the US Department of Energy Advanced Simulation and Computing program. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under Contract DE-NA0003525. Los Alamos National Laboratory is operated by Triad National Security, LLC, for the National Nuclear Security Administration of the U.S. Department of Energy (Contract No. 89233218NCA000001). This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the view of the U.S. Department of Energy or the United States Government. This manuscript has been partially authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. This material is based upon work supported in part by the US Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Robinson Pino, program manager, under Contract Number DE-AC05-00OR22725. This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Robinson Pino, program manager, under Award Number DE-SC0022566. This material is based upon work supported in part by the US Department of Energy Advanced Simulation and Computing program. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under Contract DE-NA0003525. Los Alamos National Laboratory is operated by Triad National Security, LLC, for the National Nuclear Security Administration of the U.S. Department of Energy (Contract No. 89233218NCA000001). This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the view of the U.S. Department of Energy or the United States Government. This manuscript has been partially authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ). This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

Funders	Funder number
DOE Public Access Plan
United States Government
U.S. Department of Energy	DE-SC0022566
Office of Science
National Nuclear Security Administration	89233218NCA000001, DE-NA0003525
Advanced Scientific Computing Research	DE-AC05-00OR22725

Keywords

graph algorithms
neuromorphic computing
optimization
spiking neural networks

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10.1088/2634-4386/ac889c

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Aimone, J. B., Date, P., Fonseca-Guerra, G. A., Hamilton, K. E., Henke, K., Kay, B., Kenyon, G. T., Kulkarni, S. R., Mniszewski, S. M., Parsa, M., Risbud, S. R., Schuman, C. D., Severa, W., & Smith, J. D. (2022). A review of non-cognitive applications for neuromorphic computing. Neuromorphic Computing and Engineering, 2(3), Article 032003. https://doi.org/10.1088/2634-4386/ac889c

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abstract = "Though neuromorphic computers have typically targeted applications in machine learning and neuroscience ({\textquoteleft}cognitive{\textquoteright} applications), they have many computational characteristics that are attractive for a wide variety of computational problems. In this work, we review the current state-of-the-art for non-cognitive applications on neuromorphic computers, including simple computational kernels for composition, graph algorithms, constrained optimization, and signal processing. We discuss the advantages of using neuromorphic computers for these different applications, as well as the challenges that still remain. The ultimate goal of this work is to bring awareness to this class of problems for neuromorphic systems to the broader community, particularly to encourage further work in this area and to make sure that these applications are considered in the design of future neuromorphic systems.",

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AU - Henke, Kyle

AU - Kay, Bill

AU - Kenyon, Garrett T.

AU - Kulkarni, Shruti R.

AU - Mniszewski, Susan M.

AU - Parsa, Maryam

AU - Risbud, Sumedh R.

AU - Schuman, Catherine D.

AU - Severa, William

AU - Smith, J. Darby

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N2 - Though neuromorphic computers have typically targeted applications in machine learning and neuroscience (‘cognitive’ applications), they have many computational characteristics that are attractive for a wide variety of computational problems. In this work, we review the current state-of-the-art for non-cognitive applications on neuromorphic computers, including simple computational kernels for composition, graph algorithms, constrained optimization, and signal processing. We discuss the advantages of using neuromorphic computers for these different applications, as well as the challenges that still remain. The ultimate goal of this work is to bring awareness to this class of problems for neuromorphic systems to the broader community, particularly to encourage further work in this area and to make sure that these applications are considered in the design of future neuromorphic systems.

AB - Though neuromorphic computers have typically targeted applications in machine learning and neuroscience (‘cognitive’ applications), they have many computational characteristics that are attractive for a wide variety of computational problems. In this work, we review the current state-of-the-art for non-cognitive applications on neuromorphic computers, including simple computational kernels for composition, graph algorithms, constrained optimization, and signal processing. We discuss the advantages of using neuromorphic computers for these different applications, as well as the challenges that still remain. The ultimate goal of this work is to bring awareness to this class of problems for neuromorphic systems to the broader community, particularly to encourage further work in this area and to make sure that these applications are considered in the design of future neuromorphic systems.

KW - graph algorithms

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A review of non-cognitive applications for neuromorphic computing

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Keywords

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