Binarized SNNs: Efficient and Error-Resilient Spiking Neural Networks through Binarization-Reference-Cited by-同舟云学术

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Binarized SNNs: Efficient and Error-Resilient Spiking Neural Networks through Binarization

Published:2021-11-01 Issue: Volume: Page:
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Container-title:2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD)
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Author:

Wei Ming-Liang,Yayla Mikail,Ho Shu-Yin,Chen Jiap-Jia,Yang Chia-Lin,Amrouch Hussam

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9643423/9643432/09643463.pdf?arnumber=9643463

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Indigenous back-end-of-line compatible SiO₂-based One-Time Programmable Memory for Secured Spiking Neural Network Inference Accelerator;2024 8th IEEE Electron Devices Technology & Manufacturing Conference (EDTM);2024-03-03

2. Unlocking Efficiency in BNNs: Global by Local Thresholding for Analog-Based HW Accelerators;IEEE Journal on Emerging and Selected Topics in Circuits and Systems;2023-12

3. Impact of Non-Volatile Memory Cells on Spiking Neural Network Annealing Machine With In-Situ Synapse Processing;IEEE Transactions on Circuits and Systems I: Regular Papers;2023-11

4. Towards Effective Training of Robust Spiking Recurrent Neural Networks Under General Input Noise via Provable Analysis;2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD);2023-10-28

5. Reliable Brain-inspired AI Accelerators using Classical and Emerging Memories;2023 IEEE 41st VLSI Test Symposium (VTS);2023-04-24

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