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...
Description complète
Détails bibliographiques
| Publié dans: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 44(2022), 10 vom: 16. Okt., Seite 6224-6239
|
|---|
| Auteur principal: |
Niu, Wei
(Auteur) |
|---|
| Autres auteurs: |
Li, Zhengang,
Ma, Xiaolong,
Dong, Peiyan,
Zhou, Gang,
Qian, Xuehai,
Lin, Xue,
Wang, Yanzhi,
Ren, Bin |
|---|
| Format: | Article en ligne
|
|---|
| Langue: | English |
|---|
| Publié: |
2022
|
|---|
| Accès à la collection: | IEEE transactions on pattern analysis and machine intelligence
|
|---|
| Sujets: | Journal Article
Research Support, U.S. Gov't, Non-P.H.S. |
|---|