A Quantitative Exploration of Collaborative Pruning and Approximation Computing Towards Energy Efficient Neural Networks
文献类型：期刊论文
| 作者 | He, Xin1; Yan, Guihai2; Lu, Wenyan3; Zhang, Xuan4; Liu, Ke5 |
| 刊名 | IEEE DESIGN & TEST
 |
| 出版日期 | 2020-02-01 |
| 卷号 | 37期号:1页码:36-45 |
| 关键词 | Resilience Energy consumption Approximate computing Collaboration Computational modeling Artificial neural networks Optimization Neural network Energy efficient computing Network pruning Approximate computing |
| ISSN号 | 2168-2356 |
| DOI | 10.1109/MDAT.2019.2943575 |
| 英文摘要 | Editor's note: This work has the goal of minimizing digital neural network computation energy consumption with little loss in accuracy. The authors describe a Dynamic Network Surgery based approach to network pruning, after which weights are incrementally selected for approximate multiplication. Considering which network components are necessary and determining the needed level of accuracy for them enables greater energy savings than solving either problem independently. - Robert P. Dick, University of Michigan |
| 资助项目 | National Science Foundation (NSF)[CNS-1739643] ; National Natural Science Foundation of China[61872336] ; National Natural Science Foundation of China[61572470] ; Youth Innovation Promotion Association, Chinese Academy of Science[Y404441000] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000515556300005 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/14534]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | He, Xin |
| 作者单位 | 1.Univ Michigan, Ann Arbor, MI 48109 USA 2.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing, Peoples R China 4.Washington Univ, St Louis, MO 63110 USA 5.Washington Univ, Elect & Syst Engn Dept, St Louis, MO 63110 USA |
| 推荐引用方式 GB/T 7714 | He, Xin,Yan, Guihai,Lu, Wenyan,et al. A Quantitative Exploration of Collaborative Pruning and Approximation Computing Towards Energy Efficient Neural Networks[J]. IEEE DESIGN & TEST,2020,37(1):36-45. |
| APA | He, Xin,Yan, Guihai,Lu, Wenyan,Zhang, Xuan,&Liu, Ke.(2020).A Quantitative Exploration of Collaborative Pruning and Approximation Computing Towards Energy Efficient Neural Networks.IEEE DESIGN & TEST,37(1),36-45. |
| MLA | He, Xin,et al."A Quantitative Exploration of Collaborative Pruning and Approximation Computing Towards Energy Efficient Neural Networks".IEEE DESIGN & TEST 37.1(2020):36-45. |
入库方式： OAI收割
来源：计算技术研究所
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