Exploiting computation skip to reduce energy consumption by approximate computing, an HEVC encoder case study

Approximate computing paradigm provides methods to optimize algorithms with considering both computational accuracy and complexity. This paradigm can be exploited at different levels of abstraction, from technological to application levels. Approximate computing at algorithm level aims at reducing computational complexity by approximating or skipping block functions of the computation. Numerous applications in the signal and image processing domain integrate algorithms based on discrete optimization techniques. These techniques minimize a cost function by exploring the search space. In this paper, a new approach is proposed to exploit the computation-skipping approximate computing concept by using the Smart Search Space Reduction (Sssr) technique. Sssr enables early selection of the best candidate configurations to reduce the search space. An efficient SSSR technique adjusts configuration selectivity to reduce execution complexity while selecting the most suitable functions to skip. The High Efficiency Video Coding (HEVC) encoder in All Intra (AI) profile is used as a case study to illustrate the benefits of SSSR. In this application, two functions use discrete optimization to explore different solutions and select the one leading to the minimal cost in terms of bitrate/quality and computational energy: coding-tree partitioning and intra-mode prediction. By applying SSSR to this use case, energy reductions from 20% to 70% are explored through Pareto in Rate-Energy space.