Power/QoS-Adaptive HEVC FME Hardware using Machine Learning-Based Approximation Control

This paper presents a machine learning-based adaptive approximate hardware design targeting the fractional motion estimation (FME) of HEVC encoder. Hardware designs targeting multiple levels of approximation are proposed, by changing FME filters coefficients and/or discarding taps. The level of approximation is defined by a decision tree, generated taking into account the behavior of several parameters of the encoding in order to predict homogeneous blocks, more suitable for more aggressive approximation without significant losses on quality of service (QoS). Instead of applying a specific level of approximation over the full video, different approximate FME accelerators are dynamically selected. Such a strategy is able to provide up to 50.54% of power reduction while keeping the QoS losses at 1.18% BD-BR.