Zobrazeno 1 - 10
of 1 247
pro vyhledávání: '"Liu, Zhi‐gang"'
Autor:
Huang, Zi-Lu, Liu, Zhi-Gang, Lin, Qi, Tao, Ya-Lan, Li, Xinzhuoyun, Baxter, Patricia, Su, Jack MF, Adesina, Adekunle M., Man, Chris, Chintagumpala, Murali, Teo, Wan Yee, Du, Yu-Chen, Xia, Yun-Fei, Li, Xiao-Nan
Publikováno v:
In Translational Oncology July 2024 45
Exploiting sparsity is a key technique in accelerating quantized convolutional neural network (CNN) inference on mobile devices. Prior sparse CNN accelerators largely exploit un-structured sparsity and achieve significant speedups. Due to the unbound
Externí odkaz:
http://arxiv.org/abs/2107.07983
Autor:
Zhang, Yue, Chen, Zheng-Hong, Zhao, Kun, Mu, Yu-Dong, Li, Kun-Long, Yuan, Zhi-Min, Liu, Zhi-Gang, Han, Le, Lü, Wei-Dong
Publikováno v:
In Materials Today Bio April 2024 25
Autor:
Wu, Wu-Chen, Liang, Xiao-Yu, Zhang, Dong-Ming, Jin, Liang, Liu, Zhi-Gang, Zeng, Xiao-Lu, Zhai, Qiong-Xiang, Liao, Wei-Ping, He, Na, Meng, Xiang-Hong
Publikováno v:
In Seizure: European Journal of Epilepsy March 2024 116:119-125
Autor:
Yang, Jie-Hua, Liu, Zhi-Gang, Liu, Chun-Ling, Zhang, Ming-Rui, Jia, Yan-Lu, Zhai, Qiong-Xiang, He, Ming-Feng, He, Na, Qiao, Jing-Da
Publikováno v:
In Seizure: European Journal of Epilepsy March 2024 116:30-36
Convolutional neural network (CNN) inference on mobile devices demands efficient hardware acceleration of low-precision (INT8) general matrix multiplication (GEMM). Exploiting data sparsity is a common approach to further accelerate GEMM for CNN infe
Externí odkaz:
http://arxiv.org/abs/2009.02381
Autor:
Liu, Zhi-Gang, Mattina, Matthew
Prior research has shown that Winograd algorithm can reduce the computational complexity of convolutional neural networks (CNN) with weights and activations represented in floating point. However it is difficult to apply the scheme to the inference o
Externí odkaz:
http://arxiv.org/abs/2007.12216
Convolutional neural network (CNN) inference on mobile devices demands efficient hardware acceleration of low-precision (INT8) general matrix multiplication (GEMM). The systolic array (SA) is a pipelined 2D array of processing elements (PEs), with ve
Externí odkaz:
http://arxiv.org/abs/2005.08098
Publikováno v:
Presented at On-device Intelligence Workshop at Third Conference on Machine Learning and Systems (MLSys) 2020
Kronecker Products (KP) have been used to compress IoT RNN Applications by 15-38x compression factors, achieving better results than traditional compression methods. However when KP is applied to large Natural Language Processing tasks, it leads to s
Externí odkaz:
http://arxiv.org/abs/2001.08896
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.