CoAxNN: Optimizing on-device deep learning with conditional approximate neural networks
文献类型：期刊论文
| 作者 | Li, Guangli2,3; Ma, Xiu1,4; Yu, Qiuchu2,3; Liu, Lei1,4; Liu, Huaxiao1,4; Wang, Xueying2,3 |
| 刊名 | JOURNAL OF SYSTEMS ARCHITECTURE
 |
| 出版日期 | 2023-10-01 |
| 卷号 | 143页码:14 |
| ISSN号 | 1383-7621 |
| 关键词 | On-device deep learning Efficient neural networks Model approximation and optimization |
| DOI | 10.1016/j.sysarc.2023.102978 |
| 英文摘要 | While deep neural networks have achieved superior performance in a variety of intelligent applications, the increasing computational complexity makes them difficult to be deployed on resource-constrained devices. To improve the performance of on-device inference, prior studies have explored various approximate strategies, such as neural network pruning, to optimize models based on different principles. However, when combining these approximate strategies, a large parameter space needs to be explored. Meanwhile, different configuration parameters may interfere with each other, damaging the performance optimization effect. In this paper, we propose a novel model optimization framework, CoAxNN, which effectively combines different approximate strategies, to facilitate on-device deep learning via model approximation. Based on the principles of different approximate optimizations, our approach constructs the design space and automatically finds reasonable configurations through genetic algorithm-based design space exploration. By combining the strengths of different approximation methods, CoAxNN enables efficient conditional inference for models at runtime. We evaluate our approach by leveraging state-of-the-art neural networks on a representative intelligent edge platform, Jetson AGX Orin. The experimental results demonstrate the effectiveness of CoAxNN, which achieves up to 1.53x speedup while reducing energy by up to 34.61%, with trivial accuracy loss on CIFAR-10/100 and CINIC-10 datasets. |
| 资助项目 | National Key Ramp;D Program of China[2021ZD0110101] ; National Natural Science Foundation of China[62232015] ; National Natural Science Foundation of China[62302479] ; China Postdoctoral Science Foundation[2023M733566] ; CCF-Baidu Open Fund, China |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:001071128000001 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/21145]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Wang, Xueying |
| 作者单位 | 1.Jilin Univ, Coll Comp Sci & Technol, Changchun, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, State Key Lab Processors, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Jilin Univ, MOE Key Lab Symbol Computat & Knowledge Engn, Changchun, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Guangli,Ma, Xiu,Yu, Qiuchu,et al. CoAxNN: Optimizing on-device deep learning with conditional approximate neural networks[J]. JOURNAL OF SYSTEMS ARCHITECTURE,2023,143:14. |
| APA | Li, Guangli,Ma, Xiu,Yu, Qiuchu,Liu, Lei,Liu, Huaxiao,&Wang, Xueying.(2023).CoAxNN: Optimizing on-device deep learning with conditional approximate neural networks.JOURNAL OF SYSTEMS ARCHITECTURE,143,14. |
| MLA | Li, Guangli,et al."CoAxNN: Optimizing on-device deep learning with conditional approximate neural networks".JOURNAL OF SYSTEMS ARCHITECTURE 143(2023):14. |
入库方式： OAI收割
来源：计算技术研究所
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