Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Framework-Reference-Cited by-同舟云学术

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Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Framework

Published:2021-02 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA)
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Author:

Chang Sung-En,Li Yanyu,Sun Mengshu,Shi Runbin,So Hayden K.-H.,Qian Xuehai,Wang Yanzhi,Lin Xue

Funder

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9406784/9407034/09407035.pdf?arnumber=9407035

Cited by 40 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. Tailor : Altering Skip Connections for Resource-Efficient Inference;ACM Transactions on Reconfigurable Technology and Systems;2024-01-27

2. Designing Deep Learning Models on FPGA with Multiple Heterogeneous Engines;ACM Transactions on Reconfigurable Technology and Systems;2024-01-27

3. Revisit and Benchmarking of Automated Quantization Toward Fair Comparison;IEEE Transactions on Computers;2024-01

4. Low-Area and Low-Power VLSI Architectures for Long Short-Term Memory Networks;IEEE Journal on Emerging and Selected Topics in Circuits and Systems;2023-12

5. PSQ: An Automatic Search Framework for Data-Free Quantization on PIM-based Architecture;2023 IEEE 41st International Conference on Computer Design (ICCD);2023-11-06

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