Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Zhenghao Peng"'
Publikováno v:
2022 International Joint Conference on Neural Networks (IJCNN).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cb1fe8102cfa84eb5f11c3cb0282bce5
https://doi.org/10.1109/ijcnn55064.2022.9892004
https://doi.org/10.1109/ijcnn55064.2022.9892004
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031198083
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4ded4b47aaf752fb065203497e911c0f
https://doi.org/10.1007/978-3-031-19809-0_7
https://doi.org/10.1007/978-3-031-19809-0_7
Autor:
Hai Zhao, Zhenghao Peng, Xiaoyao Liang, Zhuoran Song, Ru Wang, Dongyu Ru, Hongru Huang, Li Jiang
Publikováno v:
DATE
The training phases of Deep neural network (DNN) consumes enormous processing time and energy. Compression techniques utilizing the sparsity of DNNs can effectively accelerate the inference phase of DNNs. However, it can be hardly used in the trainin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::55da1e883b121cb2a3d65f18a9a71603
https://doi.org/10.23919/date.2019.8715135
https://doi.org/10.23919/date.2019.8715135
Publikováno v:
ICCAD
Neural network based approximate computing is a universal architecture promising to gain tremendous energy-efficiency for many error resilient applications. To guarantee the approximation quality, existing works deploy two neural networks (NNs), e.g.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0df219bc3bbb6dad80b24044ec30205d