SyncNN: Evaluating and Accelerating Spiking Neural Networks on FPGAs-Reference-Cited by-同舟云学术

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SyncNN: Evaluating and Accelerating Spiking Neural Networks on FPGAs

Published:2021-08 Issue: Volume: Page:
ISSN:
Container-title:2021 31st International Conference on Field-Programmable Logic and Applications (FPL)
language:
Short-container-title:

Author:

Panchapakesan Sathish,Fang Zhenman,Li Jian

Funder

Natural Sciences and Engineering Research Council of Canada

Canada Foundation for Innovation

Simon Fraser University

Xilinx

Nvidia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9556314/9556321/09556367.pdf?arnumber=9556367

Cited by 8 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. DeepFire2: A Convolutional Spiking Neural Network Accelerator on FPGAs;IEEE Transactions on Computers;2023-10

2. FireFly: A High-Throughput Hardware Accelerator for Spiking Neural Networks With Efficient DSP and Memory Optimization;IEEE Transactions on Very Large Scale Integration (VLSI) Systems;2023-08

3. Razor SNN: Efficient Spiking Neural Network with Temporal Embeddings;Artificial Neural Networks and Machine Learning – ICANN 2023;2023

4. A Configurable Spiking Convolution Architecture Supporting Multiple Coding Schemes on FPGA;IEEE Transactions on Circuits and Systems II: Express Briefs;2022-12

5. Energy Efficient FPGA Implementation of a Spiking Neural Network;2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT);2022-10-07

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