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pro vyhledávání: '"Xu, Shoukai"'
The conventional deep learning paradigm often involves training a deep model on a server and then deploying the model or its distilled ones to resource-limited edge devices. Usually, the models shall remain fixed once deployed (at least for some peri
Externí odkaz:
http://arxiv.org/abs/2402.17316
Autor:
Xu, Shoukai, Yao, Jiangchao, Luo, Ran, Zhang, Shuhai, Lian, Zihao, Tan, Mingkui, Han, Bo, Wang, Yaowei
Vision foundation models exhibit impressive power, benefiting from the extremely large model capacity and broad training data. However, in practice, downstream scenarios may only support a small model due to the limited computational resources or eff
Externí odkaz:
http://arxiv.org/abs/2304.02263
Autor:
Zhang, Xiaoyan a, b, Sun, Guodong b, c, ⁎⁎, Zhang, Pengfei b, Xu, Junjie b, Zhang, Xingbo a, Gao, Hongming d, Xie, Hangda a, b, Xu, Shoukai b, Zhao, Jiantuo b, Li, Pengyuan b, Wang, Lianwen a, ⁎⁎⁎, Li, Mingyang b, c, ⁎
Publikováno v:
In Materials Science & Engineering A February 2025 923
Autor:
Xu, Shoukai, Li, Haokun, Zhuang, Bohan, Liu, Jing, Cao, Jiezhang, Liang, Chuangrun, Tan, Mingkui
Neural network quantization is an effective way to compress deep models and improve their execution latency and energy efficiency, so that they can be deployed on mobile or embedded devices. Existing quantization methods require original data for cal
Externí odkaz:
http://arxiv.org/abs/2003.03603
Akademický článek
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