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Benchmarking Neuromorphic Hardware and Its Energy Expenditure
We propose and discuss a platform overarching benchmark suite for neuromorphic hardware. This suite covers benchmarks from low-level characterization to high-level application evaluation using benchmark specific metrics. With this rather broad approach we are able to compare various hardware systems...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9201569/ https://www.ncbi.nlm.nih.gov/pubmed/35720731 http://dx.doi.org/10.3389/fnins.2022.873935 |
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author | Ostrau, Christoph Klarhorst, Christian Thies, Michael Rückert, Ulrich |
author_facet | Ostrau, Christoph Klarhorst, Christian Thies, Michael Rückert, Ulrich |
author_sort | Ostrau, Christoph |
collection | PubMed |
description | We propose and discuss a platform overarching benchmark suite for neuromorphic hardware. This suite covers benchmarks from low-level characterization to high-level application evaluation using benchmark specific metrics. With this rather broad approach we are able to compare various hardware systems including mixed-signal and fully digital neuromorphic architectures. Selected benchmarks are discussed and results for several target platforms are presented revealing characteristic differences between the various systems. Furthermore, a proposed energy model allows to combine benchmark performance metrics with energy efficiency. This model enables the prediction of the energy expenditure of a network on a target system without actually having access to it. To quantify the efficiency gap between neuromorphics and the biological paragon of the human brain, the energy model is used to estimate the energy required for a full brain simulation. This reveals that current neuromorphic systems are at least four orders of magnitude less efficient. It is argued, that even with a modern fabrication process, two to three orders of magnitude are remaining. Finally, for selected benchmarks the performance and efficiency of the neuromorphic solution is compared to standard approaches. |
format | Online Article Text |
id | pubmed-9201569 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92015692022-06-17 Benchmarking Neuromorphic Hardware and Its Energy Expenditure Ostrau, Christoph Klarhorst, Christian Thies, Michael Rückert, Ulrich Front Neurosci Neuroscience We propose and discuss a platform overarching benchmark suite for neuromorphic hardware. This suite covers benchmarks from low-level characterization to high-level application evaluation using benchmark specific metrics. With this rather broad approach we are able to compare various hardware systems including mixed-signal and fully digital neuromorphic architectures. Selected benchmarks are discussed and results for several target platforms are presented revealing characteristic differences between the various systems. Furthermore, a proposed energy model allows to combine benchmark performance metrics with energy efficiency. This model enables the prediction of the energy expenditure of a network on a target system without actually having access to it. To quantify the efficiency gap between neuromorphics and the biological paragon of the human brain, the energy model is used to estimate the energy required for a full brain simulation. This reveals that current neuromorphic systems are at least four orders of magnitude less efficient. It is argued, that even with a modern fabrication process, two to three orders of magnitude are remaining. Finally, for selected benchmarks the performance and efficiency of the neuromorphic solution is compared to standard approaches. Frontiers Media S.A. 2022-06-02 /pmc/articles/PMC9201569/ /pubmed/35720731 http://dx.doi.org/10.3389/fnins.2022.873935 Text en Copyright © 2022 Ostrau, Klarhorst, Thies and Rückert. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Ostrau, Christoph Klarhorst, Christian Thies, Michael Rückert, Ulrich Benchmarking Neuromorphic Hardware and Its Energy Expenditure |
title | Benchmarking Neuromorphic Hardware and Its Energy Expenditure |
title_full | Benchmarking Neuromorphic Hardware and Its Energy Expenditure |
title_fullStr | Benchmarking Neuromorphic Hardware and Its Energy Expenditure |
title_full_unstemmed | Benchmarking Neuromorphic Hardware and Its Energy Expenditure |
title_short | Benchmarking Neuromorphic Hardware and Its Energy Expenditure |
title_sort | benchmarking neuromorphic hardware and its energy expenditure |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9201569/ https://www.ncbi.nlm.nih.gov/pubmed/35720731 http://dx.doi.org/10.3389/fnins.2022.873935 |
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