Zobrazeno 1 - 10
of 75
pro vyhledávání: '"Wang, ErWei"'
Autor:
Joksas, Dovydas, Wang, Erwei, Barmpatsalos, Nikolaos, Ng, Wing H., Kenyon, Anthony J., Constantinides, George A., Mehonic, Adnan
Publikováno v:
Adv. Sci. 2022, 2105784
Recent years have seen a rapid rise of artificial neural networks being employed in a number of cognitive tasks. The ever-increasing computing requirements of these structures have contributed to a desire for novel technologies and paradigms, includi
Externí odkaz:
http://arxiv.org/abs/2112.06887
Autor:
Wang, Erwei, Davis, James J., Stavrou, Georgios-Ilias, Cheung, Peter Y. K., Constantinides, George A., Abdelfattah, Mohamed S.
FPGA-specific DNN architectures using the native LUTs as independently trainable inference operators have been shown to achieve favorable area-accuracy and energy-accuracy tradeoffs. The first work in this area, LUTNet, exhibited state-of-the-art per
Externí odkaz:
http://arxiv.org/abs/2112.02346
Autor:
Que, Zhiqiang, Wang, Erwei, Marikar, Umar, Moreno, Eric, Ngadiuba, Jennifer, Javed, Hamza, Borzyszkowski, Bartłomiej, Aarrestad, Thea, Loncar, Vladimir, Summers, Sioni, Pierini, Maurizio, Cheung, Peter Y, Luk, Wayne
This paper presents novel reconfigurable architectures for reducing the latency of recurrent neural networks (RNNs) that are used for detecting gravitational waves. Gravitational interferometers such as the LIGO detectors capture cosmic events such a
Externí odkaz:
http://arxiv.org/abs/2106.14089
Autor:
Wang, Erwei, Davis, James J., Moro, Daniele, Zielinski, Piotr, Lim, Jia Jie, Coelho, Claudionor, Chatterjee, Satrajit, Cheung, Peter Y. K., Constantinides, George A.
The ever-growing computational demands of increasingly complex machine learning models frequently necessitate the use of powerful cloud-based infrastructure for their training. Binary neural networks are known to be promising candidates for on-device
Externí odkaz:
http://arxiv.org/abs/2102.04270
Autor:
Wang, Jiansheng1,2 (AUTHOR) wjshgl2023@163.com, Wang, Erwei3 (AUTHOR), Cheng, Shiping1,2 (AUTHOR), Ma, Aichu3 (AUTHOR)
Publikováno v:
BMC Plant Biology. 4/11/2024, Vol. 24 Issue 1, p1-14. 14p.
Autor:
Huangfu, Cheng1 (AUTHOR), Wang, Erwei2,3 (AUTHOR), Yi, Ting3 (AUTHOR), Qin, Liang2,3 (AUTHOR) qinliang@whu.edu.cn
Publikováno v:
Energies (19961073). Mar2024, Vol. 17 Issue 5, p1115. 19p.
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. Network binarization on FPGAs greatly incre
Externí odkaz:
http://arxiv.org/abs/1910.12625
Autor:
Zhao, Yiren, Gao, Xitong, Guo, Xuan, Liu, Junyi, Wang, Erwei, Mullins, Robert, Cheung, Peter Y. K., Constantinides, George, Xu, Cheng-Zhong
Modern deep Convolutional Neural Networks (CNNs) are computationally demanding, yet real applications often require high throughput and low latency. To help tackle these problems, we propose Tomato, a framework designed to automate the process of gen
Externí odkaz:
http://arxiv.org/abs/1910.10075
Publikováno v:
In Physica B: Condensed Matter 1 July 2023 660
Publikováno v:
In Journal of Solid State Chemistry July 2023 323