| Autor: |
Jia-Ying Lin, Tung-Hsing Wu, Han-Liang Chou, Chi-cheng Ju, Chen Li-Heng, Tsu-Ming Liu, Chang-Hung Tsai, Yung-Chang Chang |
| Rok vydání: |
2018 |
| Předmět: |
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| Zdroj: |
IEEE Solid-State Circuits Letters. 1:221-224 |
| ISSN: |
2573-9603 |
| DOI: |
10.1109/lssc.2019.2905958 |
| Popis: |
This letter proposes the world’s first deep learning (DL)-assisted video encoder LSI fabricated in a 10-nm process with a core area of 0.76 mm2 to integrate quad-core DL accelerators and $4\text{K}\times 2\text{K}$ H.264/H.265 video encoders. A visual-contact-field network (VCFNet) DL model is newly designed to predict human focus information with extraordinary reduction of encoding complexity, leading to 82.3% power reduction. Moreover, input channel reduction and layer merging approaches reduce VCFNet complexity by 69%. Operated at 0.9 V and 504 MHz, the proposed DL-assisted 4K video encoder LSI consumes 56.54 mW to achieve 0.22 nJ/pixel of energy efficiency, cutting 0.1-14 nJ/pixel compared to conventional designs. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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