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FILM-QNN: Efficient FPGA Acceleration of Deep Neural Networks with Intra-Layer, Mixed-Precision Quantization

M. Sun, Z. Li, A. Lu, Y. Li, S. Chang, X. Ma, X. Lin, and Z. Fang. FPGA '22: The 2022 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, Virtual Event, USA, 27 February 2022 - 1 March 2022, page 134--145. ACM, (2022)
DOI: 10.1145/3490422.3502364

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Fang-Fang Tang

Fang Chen

Xing Fang

Weigui Fang

Xiangfan Fang

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HeatViT: Hardware-Efficient Adaptive Token Pruning for Vision Transformers.P. Dong, M. Sun, A. Lu, Y. Xie, K. Liu, Z. Kong, X. Meng, Z. Li, X. Lin, Z. Fang and 1 other author(s). HPCA, page 442-455. IEEE, (2023)LEAP: A Deep Learning based Aging-Aware Architecture Exploration Framework for FPGAs.B. Ghavami, S. Ebrahimi, Z. Fang, and L. Shannon. FPGA, page 146. ACM, (2021)Caffeine: towards uniformed representation and acceleration for deep convolutional neural networks.C. Zhang, Z. Fang, P. Zhou, P. Pan, and J. Cong. ICCAD, page 12:1-12:8. ACM, (2016)Supporting Address Translation for Accelerator-Centric Architectures.Y. Hao, Z. Fang, G. Reinman, and J. Cong. HPCA, page 37-48. IEEE Computer Society, (2017)ARAPrototyper: Enabling Rapid Prototyping and Evaluation for Accelerator-Rich Architecture (Abstact Only).Y. Chen, J. Cong, Z. Fang, and P. Zhou. FPGA, page 281. ACM, (2016)Measuring Microarchitectural Details of Multi- and Many-Core Memory Systems through Microbenchmarking.Z. Fang, S. Mehta, P. Yew, A. Zhai, J. Greensky, G. Beeraka, and B. Zang. ACM Trans. Archit. Code Optim., 11 (4): 55:1-55:26 (2014)A quantitative analysis on microarchitectures of modern CPU-FPGA platforms.Y. kyu Choi, J. Cong, Z. Fang, Y. Hao, G. Reinman, and P. Wei. DAC, page 109:1-109:6. ACM, (2016)FILM-QNN: Efficient FPGA Acceleration of Deep Neural Networks with Intra-Layer, Mixed-Precision Quantization.M. Sun, Z. Li, A. Lu, Y. Li, S. Chang, X. Ma, X. Lin, and Z. Fang. FPGA, page 134-145. ACM, (2022)HyBNN: Quantifying and Optimizing Hardware Efficiency of Binary Neural Networks.G. Yang, J. Lei, Z. Fang, Y. Li, J. Zhang, and W. Xie. FCCM, page 203. IEEE, (2023)Journal Track Paper ICFPT 2023 : HyBNN: Quantifying and Optimizing Hardware Efficiency of Binary Neural Networks.G. Yang, J. Lei, Z. Fang, Y. Li, J. Zhang, and W. Xie. ICFPT, page 3-4. IEEE, (2023)

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Disambiguation of "Fang, Zhenman"

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FILM-QNN: Efficient FPGA Acceleration of Deep Neural Networks with Intra-Layer, Mixed-Precision Quantization

Please choose a person to relate this publication to

Fang-Fang Tang

Fang Chen

Xing Fang

Weigui Fang

Xiangfan Fang

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Fang, Zhenman"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML FILM-QNN: Efficient FPGA Acceleration of Deep Neural Networks with Intra-Layer, Mixed-Precision Quantization

Please choose a person to relate this publication to

Fang-Fang Tang

Fang Chen

Xing Fang

Weigui Fang

Xiangfan Fang

Other publications of authors with the same name

Disambiguation

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DOI
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FILM-QNN: Efficient FPGA Acceleration of Deep Neural Networks with Intra-Layer, Mixed-Precision Quantization