An Efficient SNN Model Generation Method for IoT Edge Computing-Reference-Cited by-同舟云学术

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An Efficient SNN Model Generation Method for IoT Edge Computing

Published:2024-04-08 Issue: Volume: Page:1542-1543
ISSN:
Container-title:Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing
language:
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Author:

Kim Seoyeon¹^ORCID,Cho Jinsung²^ORCID,Kim Bongjae³^ORCID,Jung Jinman⁴^ORCID

Affiliation:

1. Institute of Human-Centered Computing, Inha University, Incheon, Republic of Korea

2. Korea Electronics Technology Institute, Seongnam, Republic of Korea

3. Department of Computer Engineering, Chungbuk National University, Cheongju, Republic of Korea

4. Department of Computer Engineering, Inha University, Incheon, Republic of Korea

Funder

National Research Foundation of Korea

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3605098.3636159

Reference7 articles.

1. Nguyen, T. N. N., Veeravalli, B., & Fong, X. (2022). Hardware Implementation for Spiking Neural Networks on Edge Devices. In Predictive Analytics in Cloud, Fog, and Edge Computing: Perspectives and Practices of Blockchain, IoT, and 5G (pp. 227--248). Springer International Publishing.

2. TrueNorth: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Neurosynaptic Chip

3. Loihi: A Neuromorphic Manycore Processor with On-Chip Learning

4. SpiNNaker: Event-Based Simulation—Quantitative Behavior

5. Bekolay, T., et al. (2014). Nengo: A Python Tool for Building Large-Scale Functional Brain Models. Frontiers in Neuroinformatics, 7.

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