GRIM : A General, Real-Time Deep Learning Inference Framework for Mobile Devices Based on Fine-Grained Structured Weight Sparsity
It is appealing but challenging to achieve real-time deep neural network (DNN) inference on mobile devices, because even the powerful modern mobile devices are considered as "resource-constrained" when executing large-scale DNNs. It necessitates the sparse model inference via weight prunin...
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Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 44(2022), 10 vom: 16. Okt., Seite 6224-6239
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| 1. Verfasser: |
Niu, Wei
(VerfasserIn) |
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| Weitere Verfasser: |
Li, Zhengang,
Ma, Xiaolong,
Dong, Peiyan,
Zhou, Gang,
Qian, Xuehai,
Lin, Xue,
Wang, Yanzhi,
Ren, Bin |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2022
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence
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| Schlagworte: | Journal Article
Research Support, U.S. Gov't, Non-P.H.S. |
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