1 code implementation • 4 Dec 2021 • Erwei Wang, James J. Davis, Georgios-Ilias Stavrou, Peter Y. K. Cheung, George A. Constantinides, Mohamed S. Abdelfattah
To address these issues, we propose logic shrinkage, a fine-grained netlist pruning methodology enabling K to be automatically learned for every LUT in a neural network targeted for FPGA inference.
1 code implementation • 26 Jun 2021 • Zhiqiang Que, Erwei Wang, Umar Marikar, Eric Moreno, Jennifer Ngadiuba, Hamza Javed, Bartłomiej Borzyszkowski, Thea Aarrestad, Vladimir Loncar, Sioni Summers, Maurizio Pierini, Peter Y Cheung, Wayne Luk
The proposed approach has been evaluated based on two LSTM models, targeting a ZYNQ 7045 FPGA and a U250 FPGA.
2 code implementations • 8 Feb 2021 • Erwei Wang, James J. Davis, Daniele Moro, Piotr Zielinski, Jia Jie Lim, Claudionor Coelho, Satrajit Chatterjee, Peter Y. K. Cheung, George A. Constantinides
The ever-growing computational demands of increasingly complex machine learning models frequently necessitate the use of powerful cloud-based infrastructure for their training.
2 code implementations • 24 Oct 2019 • Erwei Wang, James J. Davis, Peter Y. K. Cheung, George A. Constantinides
Research has shown that deep neural networks contain significant redundancy, and thus that high classification accuracy can be achieved even when weights and activations are quantized down to binary values.
no code implementations • 21 Oct 2019 • Yiren Zhao, Xitong Gao, Xuan Guo, Junyi Liu, Erwei Wang, Robert Mullins, Peter Y. K. Cheung, George Constantinides, Cheng-Zhong Xu
Furthermore, we show how Tomato produces implementations of networks with various sizes running on single or multiple FPGAs.
2 code implementations • 1 Apr 2019 • Erwei Wang, James J. Davis, Peter Y. K. Cheung, George A. Constantinides
Research has shown that deep neural networks contain significant redundancy, and that high classification accuracies can be achieved even when weights and activations are quantised down to binary values.
no code implementations • 21 Jan 2019 • Erwei Wang, James J. Davis, Ruizhe Zhao, Ho-Cheung Ng, Xinyu Niu, Wayne Luk, Peter Y. K. Cheung, George A. Constantinides
Deep neural networks have proven to be particularly effective in visual and audio recognition tasks.