Skip to Main content Skip to Navigation
Journal articles

Algorithmic-level Approximate Computing Applied to Energy Efficient HEVC Decoding

Abstract : This paper presents a novel method for applying approximate computing at the level of a complete application. The method decomposes the application into processing blocks which types define the classes of approximate computing techniques they may tolerate. By applying these approximation techniques to the most computationally intensive blocks, drastic energy reduction can be obtained at a limited cost in terms of Quality of Service. The algorithmic-level approximate computing method is applied to a software HEVC video decoder. The method is shown to offer multiple trade-offs between the quality of the decoded video and the energy required for the decoding process. The algorithmic-level approximate computing method offers new possibilities in terms of application energy budgeting. Energy reductions of up to 40% are demonstrated for a limited degradation of the application Quality of Service.
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-01354638
Contributor : Erwan Nogues <>
Submitted on : Friday, August 19, 2016 - 10:14:56 AM
Last modification on : Thursday, December 19, 2019 - 10:54:17 AM

Identifiers

Citation

Erwan Nogues, Daniel Ménard, Maxime Pelcat. Algorithmic-level Approximate Computing Applied to Energy Efficient HEVC Decoding. IEEE Transactions on Emerging Topics in Computing, Institute of Electrical and Electronics Engineers, 2019, 7 (1), pp.5-17. ⟨10.1109/TETC.2016.2593644⟩. ⟨hal-01354638⟩

Share

Metrics

Record views

486