A machine learning-based approach for power and thermal management of next-generation video coding on MPSoCs-Reference-Cited by-同舟云学术

A machine learning-based approach for power and thermal management of next-generation video coding on MPSoCs

Published:2017-10-15 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Twelfth IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis Companion
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Author:

Iranfar Arman¹,Zapater Marina¹,Atienza David¹

Affiliation:

1. Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3125502.3125533

Reference8 articles.

1. Philippe Bordes Pierre Andrivon Franck Hiron Philippe Salmon and Ronan Boitard. 2016. Joint Collaborative Team on Video Coding (JCT-VC) of ITU-T SG 16 WP 3 and ISO/IEC JTC 1/SC 29/WG 11. (2016). https://HEVC.hhi.fraunhofer.de Philippe Bordes Pierre Andrivon Franck Hiron Philippe Salmon and Ronan Boitard. 2016. Joint Collaborative Team on Video Coding (JCT-VC) of ITU-T SG 16 WP 3 and ISO/IEC JTC 1/SC 29/WG 11. (2016). https://HEVC.hhi.fraunhofer.de

2. HEVC Complexity and Implementation Analysis

3. Cisco Systems Inc. 2016. Cisco Visual Networking Index: Forecast and Methodology 2015--2020. Cisco Whitepaper. (2016). Cisco Systems Inc. 2016. Cisco Visual Networking Index: Forecast and Methodology 2015--2020. Cisco Whitepaper. (2016).

4. Complexity scalability for real-time HEVC encoders

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