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Achieving Green AI with Energy-Efficient Deep Learning Using Neuromorphic Computing

Authors:

Tao Luo,

Weiyun YauAuthors Info & Claims

Communications of the ACM, Volume 66, Issue 7

Pages 52 - 57

https://doi.org/10.1145/3588591

Published: 22 June 2023 Publication History

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References

[1]

Fabien, C. et al. Event-based visual sensing for human motion detection and classification at various distances. In Proceedings of Pacific-Rim Symp. Image and Video Technology, 2022.

Google Scholar

[2]

Lee, M.K. F. et al. A system-level simulator for RRAM-based neuromorphic computing chips. ACM Trans. Architecture and Code Optimization 15, 4 (2019), 1--24.

Google Scholar

[3]

Luo, T. et al. An FPGA-based hardware emulator for neuromorphic chip with RRAM. IEEE Trans. Computer-Aided Design of Integrated Circuits and Systems 39, 22 (2018), 438--450.

Google Scholar

[4]

Nambiar, V.P. et al. Energy efficient 0.5 V 4.8 pJ/SOP 0.93 W leakage/core neuromorphic processor design. IEEE Trans. Circuits and Systems II: Express Briefs 68, 9 (2021), 3148--3152.

Crossref

Google Scholar

[5]

Wong, M.M. et al. A 2.1 pJ/SOP 40nm SNN accelerator featuring on-chip transfer learning using Delta STDP. In Proceedings of the IEEE 51^st European Solid-State Device Research Conf. 2021.

Google Scholar

[6]

Wu, J. et al. Progressive tandem learning for pattern recognition with deep spiking neural networks. IEEE Trans. Pattern Analysis and Machine Intelligence, 44, 11 (2021), 7824--7840.

Google Scholar

[7]

Yang, L. et al. Coreset: Hierarchical neuromorphic computing supporting large-scale neural networks with improved resource efficiency. Neurocomputing 474 (2022), 128--140.

Digital Library

Google Scholar

[8]

Zhang, M. et al. Rectified linear postsynaptic potential function for backpropagation in deep spiking neural networks. IEEE Trans. Neural Networks and Learning Systems 33, 5 (2021), 1947--1958.

Google Scholar

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Choudhury RRoy PGhosh SGhosh A(2024)Machine Learning and Deep Learning Algorithms for Green ComputingComputational Intelligence for Green Cloud Computing and Digital Waste Management10.4018/979-8-3693-1552-1.ch001(1-23)Online publication date: 27-Feb-2024
https://doi.org/10.4018/979-8-3693-1552-1.ch001
Al Abdul Wahid SAsad AMohammadi F(2024)A Survey on Neuromorphic Architectures for Running Artificial Intelligence AlgorithmsElectronics10.3390/electronics1315296313:15(2963)Online publication date: 26-Jul-2024
https://doi.org/10.3390/electronics13152963
Kannan JBarnett SSimmons ASelvi TCruz LBosch JLewis GCleland-Huang JMuccini H(2024)Green Runner: A Tool for Efficient Deep Learning Component SelectionProceedings of the IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI10.1145/3644815.3644942(112-117)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3644815.3644942

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Achieving Green AI with Energy-Efficient Deep Learning Using Neuromorphic Computing

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Communications of the ACM Volume 66, Issue 7

July 2023

126 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/3605915

Editor:
James Larus
Association for Computing Machinery, New York, NY

Issue’s Table of Contents

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 22 June 2023

Published in CACM Volume 66, Issue 7

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Singapore Government?s Research, Innovation and Enterprise 2020 Plan (Advanced Manufacturing and Engineering domain) under Grant A1687b0033.

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Choudhury RRoy PGhosh SGhosh A(2024)Machine Learning and Deep Learning Algorithms for Green ComputingComputational Intelligence for Green Cloud Computing and Digital Waste Management10.4018/979-8-3693-1552-1.ch001(1-23)Online publication date: 27-Feb-2024
https://doi.org/10.4018/979-8-3693-1552-1.ch001
Al Abdul Wahid SAsad AMohammadi F(2024)A Survey on Neuromorphic Architectures for Running Artificial Intelligence AlgorithmsElectronics10.3390/electronics1315296313:15(2963)Online publication date: 26-Jul-2024
https://doi.org/10.3390/electronics13152963
Kannan JBarnett SSimmons ASelvi TCruz LBosch JLewis GCleland-Huang JMuccini H(2024)Green Runner: A Tool for Efficient Deep Learning Component SelectionProceedings of the IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI10.1145/3644815.3644942(112-117)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3644815.3644942
Haider MHassan MAhmed IReed JRubaai ARawat D(2024)Deep Learning Aided Minimum Mean Square Error Estimation of Gaussian Source in Industrial Internet-of-Things NetworksIEEE Transactions on Industrial Cyber-Physical Systems10.1109/TICPS.2024.34208232(185-195)Online publication date: 2024
https://doi.org/10.1109/TICPS.2024.3420823

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Energy efficient hybrid policy in green cloud computing

Energy efficient task allocation and energy scheduling in green energy powered edge computing

On the role of system software in energy management of neuromorphic computing

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