Zobrazeno 1 - 10
of 156
pro vyhledávání: '"Liu, Zejian"'
Autor:
Zhu, Zeyu, Li, Fanrong, Li, Gang, Liu, Zejian, Mo, Zitao, Hu, Qinghao, Liang, Xiaoyao, Cheng, Jian
Graph Neural Networks (GNNs) are becoming a promising technique in various domains due to their excellent capabilities in modeling non-Euclidean data. Although a spectrum of accelerators has been proposed to accelerate the inference of GNNs, our anal
Externí odkaz:
http://arxiv.org/abs/2311.09775
Autor:
Zhu, Zeyu, Li, Fanrong, Mo, Zitao, Hu, Qinghao, Li, Gang, Liu, Zejian, Liang, Xiaoyao, Cheng, Jian
As graph data size increases, the vast latency and memory consumption during inference pose a significant challenge to the real-world deployment of Graph Neural Networks (GNNs). While quantization is a powerful approach to reducing GNNs complexity, m
Externí odkaz:
http://arxiv.org/abs/2302.00193
Autor:
Liu, Zejian, Li, Meng
Derivatives are a key nonparametric functional in wide-ranging applications where the rate of change of an unknown function is of interest. In the Bayesian paradigm, Gaussian processes (GPs) are routinely used as a flexible prior for unknown function
Externí odkaz:
http://arxiv.org/abs/2210.11626
Autor:
Liu, Zejian1,2 (AUTHOR) liu.zejian@hgnyjs.com, Li, Fengneng1,2 (AUTHOR) 202221016031@mail.scut.edu.cn, Yang, Ping1,2 (AUTHOR) eppyang@scut.edu.cn, Lin, Xu3 (AUTHOR) linxu@gpdc.gd.csg.cn, Zhang, Guozun4,5 (AUTHOR) zhangguozun321@stud.tjut.edu.cn
Publikováno v:
Energies (19961073). Jul2024, Vol. 17 Issue 13, p3282. 13p.
Autor:
Liu, Zejian, Liu, Gongqi, Cheng, Leilei, Gu, Jing, Yang, Jialiang, Yuan, Haoran, Chen, Yong, Wu, Yufeng
Publikováno v:
In Separation and Purification Technology 5 May 2024 335
Publikováno v:
In Green Energy & Environment May 2024 9(5):802-830
Deep convolutional neural networks have achieved remarkable progress in recent years. However, the large volume of intermediate results generated during inference poses a significant challenge to the accelerator design for resource-constraint FPGA. D
Externí odkaz:
http://arxiv.org/abs/2105.08937
There is a wide range of applications where the local extrema of a function are the key quantity of interest. However, there is surprisingly little work on methods to infer local extrema with uncertainty quantification in the presence of noise. By vi
Externí odkaz:
http://arxiv.org/abs/2103.10606
Publikováno v:
Design, Automation & Test in Europe (DATE) 2021
BERT is the most recent Transformer-based model that achieves state-of-the-art performance in various NLP tasks. In this paper, we investigate the hardware acceleration of BERT on FPGA for edge computing. To tackle the issue of huge computational com
Externí odkaz:
http://arxiv.org/abs/2103.02800
Autor:
Chen Yijun, Lu Zhujin, Feng Jiaxin, Chen Zefeng, Liu Zejian, Wang Xiuqi, Yan Huichao, Gao Chunqi
Publikováno v:
Acta Biochimica et Biophysica Sinica, Vol 55, Pp 1213-1221 (2023)
Roof plate-specific spondin 1 (R-spondin1, RSPO1) is a Wnt/β-catenin signaling pathway activator that binds with Wnt ligands to stimulate the Wnt/β-catenin signaling pathway, which is key to hair regeneration. However, it is not clear whether recom
Externí odkaz:
https://doaj.org/article/33dd55d0fb8a4bc9bcb1ae71ef42ea3c