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.
Aimone, James B., et al. "A review of non-cognitive applications for neuromorphic computing." Neuromorphic Computing and Engineering, vol. 2, no. 3, Sep. 2022. https://doi.org/10.1088/2634-4386/ac889c
Aimone, James B., Date, Prasanna, Fonseca-Guerra, Gabriel A., Hamilton, Kathleen E., Henke, Kyle, Kay, Bill, Kenyon, Garrett T., Kulkarni, Shruti R., Mniszewski, Susan M., Parsa, Maryam, Risbud, Sumedh R., Schuman, Catherine D., Severa, William, & Smith, J. Darby (2022). A review of non-cognitive applications for neuromorphic computing. Neuromorphic Computing and Engineering, 2(3). https://doi.org/10.1088/2634-4386/ac889c
Aimone, James B., Date, Prasanna, Fonseca-Guerra, Gabriel A., et al., "A review of non-cognitive applications for neuromorphic computing," Neuromorphic Computing and Engineering 2, no. 3 (2022), https://doi.org/10.1088/2634-4386/ac889c
@article{osti_1886076,
author = {Aimone, James B. and Date, Prasanna and Fonseca-Guerra, Gabriel A. and Hamilton, Kathleen E. and Henke, Kyle and Kay, Bill and Kenyon, Garrett T. and Kulkarni, Shruti R. and Mniszewski, Susan M. and Parsa, Maryam and others},
title = {A review of non-cognitive applications for neuromorphic computing},
annote = {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.},
doi = {10.1088/2634-4386/ac889c},
url = {https://www.osti.gov/biblio/1886076},
journal = {Neuromorphic Computing and Engineering},
issn = {ISSN 2634-4386},
number = {3},
volume = {2},
place = {United Kingdom},
publisher = {IOP Publishing},
year = {2022},
month = {09}}