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

Author:

Sun Mengshu1,Li Zhengang1,Lu Alec2,Li Yanyu1,Chang Sung-En1,Ma Xiaolong1,Lin Xue1,Fang Zhenman2

Affiliation:

1. Northeastern University, Boston, MA, USA

2. Simon Fraser University, Burnaby, Canada

Funder

NSERC Discovery Grant

CFI John R. Evans Leaders Fund

NSF (National Science Foundation)

Alliance Grant

Publisher

ACM

Reference57 articles.

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

3. 019)]% cheng2019uL2Q, Gong Cheng , Lu Ye , Li Tao , Zhang Xiaofan , Hao Cong , Chen Deming , and Chen Yao . 2019 . μL2Q: An Ultra-Low Loss Quantization Method for DNN . The 2019 International Joint Conference on Neural Networks (IJCNN) (2019), 1--8. 019)]% cheng2019uL2Q, Gong Cheng, Lu Ye, Li Tao, Zhang Xiaofan, Hao Cong, Chen Deming, and Chen Yao. 2019. μL2Q: An Ultra-Low Loss Quantization Method for DNN. The 2019 International Joint Conference on Neural Networks (IJCNN) (2019), 1--8.

4. 018)]% choi2018pact, Jungwook Choi , Zhuo Wang , Swagath Venkataramani , Pierce I-Jen Chuang , Vijayalakshmi Srinivasan, and Kailash Gopalakrishnan. 2018 . Pact : Parameterized clipping activation for quantized neural networks. arXiv preprint arXiv:1805.06085 (2018). 018)]% choi2018pact, Jungwook Choi, Zhuo Wang, Swagath Venkataramani, Pierce I-Jen Chuang, Vijayalakshmi Srinivasan, and Kailash Gopalakrishnan. 2018. Pact: Parameterized clipping activation for quantized neural networks. arXiv preprint arXiv:1805.06085 (2018).

5. Exploration of Low Numeric Precision Deep Learning Inference Using Intel® FPGAs

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