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Hardware Accelerators for Spiking Neural Networks for Energy-Efficient Edge Computing

Published: 05 June 2023 Publication History

Abstract

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References

[1]
Sayeed Shafayet Chowdhury, Isha Garg, and Kaushik Roy. 2021. Spatio-temporal pruning and quantization for low-latency spiking neural networks. In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 1--9.
[2]
Youngeun Kim, Yuhang Li, Hyoungseob Park, Yeshwanth Venkatesha, Ruokai Yin, and Priyadarshini Panda. 2022. Exploring lottery ticket hypothesis in spiking neural networks. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XII. Springer, 102--120.
[3]
Youngeun Kim and Priyadarshini Panda. 2021. Revisiting batch normalization for training low-latency deep spiking neural networks from scratch. Frontiers in neuroscience (2021), 1638.
[4]
Abhishek Moitra, Abhiroop Bhattacharjee, Runcong Kuang, Gokul Krishnan, Yu Cao, and Priyadarshini Panda. 2022. SpikeSim: An end-to-end Compute-in-Memory Hardware Evaluation Tool for Benchmarking Spiking Neural Networks. arXiv preprint arXiv:2210.12899 (2022).
[5]
Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, Yuan Xie, and Luping Shi. 2019. Direct training for spiking neural networks: Faster, larger, better. In Proceedings of the AAAI conference on artificial intelligence, Vol. 33. 1311--1318.
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Ruokai Yin, Abhishek Moitra, Abhiroop Bhattacharjee, Youngeun Kim, and Priyadarshini Panda. 2022. Sata: Sparsity-aware training accelerator for spiking neural networks. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (2022).
[7]
Wenrui Zhang and Peng Li. 2020. Temporal spike sequence learning via backpropagation for deep spiking neural networks. Advances in Neural Information Processing Systems, Vol. 33 (2020), 12022--12033.
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Hanle Zheng, Yujie Wu, Lei Deng, Yifan Hu, and Guoqi Li. 2021. Going deeper with directly-trained larger spiking neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 11062--11070.
[9]
Yaoyu Zhu, Zhaofei Yu, Wei Fang, Xiaodong Xie, Tiejun Huang, and Timothée Masquelier. 2022. Training Spiking Neural Networks with Event-driven Backpropagation. In 36th Conference on Neural Information Processing Systems (NeurIPS 2022).

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cover image ACM Conferences
GLSVLSI '23: Proceedings of the Great Lakes Symposium on VLSI 2023
June 2023
731 pages
ISBN:9798400701252
DOI:10.1145/3583781
Permission to make digital or hard copies of all or part 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 components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 June 2023

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Author Tags

  1. area & energy-efficiency
  2. hybrid-device architecture
  3. in-memory computing
  4. non-idealities

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  • Research-article

Funding Sources

  • National Science Foundation CAREER Award
  • TII (Abu Dhabi)
  • DARPA AI Exploration (AIE) program
  • DoE MMICC center SEA-CROGS (Award #DE-SC0023198)
  • CoCoSys, a JUMP2.0 center sponsored by DARPA and SRC
  • Google Research Scholar Award

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GLSVLSI '23
Sponsor:
GLSVLSI '23: Great Lakes Symposium on VLSI 2023
June 5 - 7, 2023
TN, Knoxville, USA

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Overall Acceptance Rate 312 of 1,156 submissions, 27%

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