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A high performance implementation of a factorized-prior image compression model
https://ipsj.ixsq.nii.ac.jp/records/220904
https://ipsj.ixsq.nii.ac.jp/records/220904d935ad89-5d5f-4de4-9f2f-cc1e2e328436
| 名前 / ファイル | ライセンス | アクション |
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Copyright (c) 2022 by the Information Processing Society of Japan
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| Item type | National Convention(1) | |||||||||||
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| 公開日 | 2022-02-17 | |||||||||||
| タイトル | ||||||||||||
| タイトル | A high performance implementation of a factorized-prior image compression model | |||||||||||
| 言語 | ||||||||||||
| 言語 | eng | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | 人工知能と認知科学 | |||||||||||
| 資源タイプ | ||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_5794 | |||||||||||
| 資源タイプ | conference paper | |||||||||||
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| 早大 | ||||||||||||
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| 早大 | ||||||||||||
| 著者所属 | ||||||||||||
| 早大 | ||||||||||||
| 著者名 |
林, 方正
× 林, 方正
× 孫, 鶴鳴
× 甲藤, 二郎
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| 論文抄録 | ||||||||||||
| 内容記述タイプ | Other | |||||||||||
| 内容記述 | We introduce a high performance implementation, based on pipelining and NVIDIA TensorRT, of a factorized-prior image compression model. While the original implementation is single-threaded and less efficient on system resources, our implementation features multi-threading pipelining for CPU intensive tasks such as entropy coding, and a TensorRT-optimized GPU model backend in FP16 precision. The implementation allows high frame rate, high throughput and low latency image compression which better utilizes compute capabilities, coming with only negligible accuracy loss compared to the original implementation. We also describe observed performance with our implementation deployed on Jetson Xavier NX, an embedded platform from NVIDIA. We observed a 75 fps encoding and 35 fps decoding throughput for 768x512-pixel images under the 20W power profile. | |||||||||||
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| 収録物識別子タイプ | NCID | |||||||||||
| 収録物識別子 | AN00349328 | |||||||||||
| 書誌情報 |
第84回全国大会講演論文集 巻 2022, 号 1, p. 317-318, 発行日 2022-02-17 |
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| 出版者 | ||||||||||||
| 言語 | ja | |||||||||||
| 出版者 | 情報処理学会 | |||||||||||