Institute of Computing Technology, Chinese Academy IR
| Lightweight real-time stereo matching algorithm for AI chips | |
| Liu, Yi1; Wang, Wenhao2,3; Xu, Xintao1; Guo, Xiaozhou1,3; Gong, Guoliang1,3; Lu, Huaxiang1,3,4,5 | |
| 2023-02-01 | |
| 发表期刊 | COMPUTER COMMUNICATIONS
 |
| ISSN | 0140-3664 |
| 卷号 | 199页码:210-217 |
| 摘要 | AI chips have developed rapidly and achieved remarkable acceleration effects in the corresponding algorithm field in recent years. However, deep learning algorithms are changing rapidly, including many operators that AI chips and inference frameworks cannot use in the short term. To solve the problem that it is challenging to deploy a stereo matching algorithm based on binocular vision on AI chips, this paper proposes a multi-stage unsupervised lightweight real-time depth estimation algorithm for AI chips called TradNet. TradNet combines the traditional matching algorithm with a convolutional neural network and uses convolution directly supported by AI chips to realize the structure of the traditional matching algorithm. TradNet is composed of operators directly supported by current AI chips, which reduces the computational complexity of the algorithm, and greatly improves the compatibility of the stereo matching algorithm with existing AI chips. Compared with the deep learning-based multi-stage binocular disparity algorithm AnyNet, the accuracy is improved by 5.12%, and the inference speed is only 12.7%. Compared with the matching-based binocular disparity algorithm BM, the accuracy is improved by 25.24%, and the inference speed is only 48.7%. Our final model can process 1280x720 resolution images within a range of 60-80 FPS on an NVIDIA TITAN Xp. It achieves 28FPS on a 1TOPS (Tera Operations Per Second) custom AI chip, and the power consumption is 0.88 W. |
| 关键词 | AI chips Stereo matching Lightweight network Unsupervised learning Multi-stage stereo matching |
| DOI | 10.1016/j.comcom.2022.06.018 |
| 收录类别 | SCI |
| 语种 | 英语 |
| 资助项目 | Beijing Academy of Artificial Intelligence (BAAI) ; CAS Strategic Leading Science and Technology Project[XDA27040303] ; CAS Strategic Leading Science and Technology Project[XDA18040400] ; CAS Strategic Leading Science and Technology Project[XDB44000000] ; National Natural Science Foundation of China[U19A 2080] ; National Natural Science Foundation of China[U1936106] ; High Technology Project[31513070501] ; High Technology Project[1916312ZD0090-2201] |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| WOS类目 | Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications |
| WOS记录号 | WOS:000916921000001 |
| 出版者 | ELSEVIER |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://119.78.100.204/handle/2XEOYT63/20011 |
| 专题 | 中国科学院计算技术研究所期刊论文 |
| 通讯作者 | Gong, Guoliang |
| 作者单位 | 1.Chinese Acad Sci, Inst Semicond, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing, Peoples R China 5.Beijing Key Lab Semicond Neural Network Intellige, Beijing, Peoples R China 6.Univ Sci & Technol China, Hefei, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Yi,Wang, Wenhao,Xu, Xintao,et al. Lightweight real-time stereo matching algorithm for AI chips[J]. COMPUTER COMMUNICATIONS,2023,199:210-217. |
| APA | Liu, Yi,Wang, Wenhao,Xu, Xintao,Guo, Xiaozhou,Gong, Guoliang,&Lu, Huaxiang.(2023).Lightweight real-time stereo matching algorithm for AI chips.COMPUTER COMMUNICATIONS,199,210-217. |
| MLA | Liu, Yi,et al."Lightweight real-time stereo matching algorithm for AI chips".COMPUTER COMMUNICATIONS 199(2023):210-217. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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