Query Time Optimized Deep Learning Based Video Inference System
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910321946394624 |
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| author | Shen, Mingren Dong, Shuoxuan He, Xiuyuan |
| author_facet | Shen, Mingren Dong, Shuoxuan He, Xiuyuan |
| contents | This is a project report about how we tune Focus[1], a video inference system that provides low cost and low latency, through two phases. In this report, we will decrease the query time by saving the middle layer output of the neural network. This is a trade-off strategy that involves using more space to save time. We show how this scheme works using prototype systems, and it saves 20% of the time. The code repository URL is here, https://github.com/iphyer/CS744 FocousIngestOpt. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2212_06905 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Query Time Optimized Deep Learning Based Video Inference System Shen, Mingren Dong, Shuoxuan He, Xiuyuan Computational Engineering, Finance, and Science This is a project report about how we tune Focus[1], a video inference system that provides low cost and low latency, through two phases. In this report, we will decrease the query time by saving the middle layer output of the neural network. This is a trade-off strategy that involves using more space to save time. We show how this scheme works using prototype systems, and it saves 20% of the time. The code repository URL is here, https://github.com/iphyer/CS744 FocousIngestOpt. |
| title | Query Time Optimized Deep Learning Based Video Inference System |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2212.06905 |