Video Decoder Improvements with Near-Data Speculative Motion Compensation Processing

Video decoder implementations are still evolving as they directly affect a large fraction of embedded systems nowadays. In this context, Versatile Video Coding (VVC) brings increased compression efficiency, which comes with extra over-head in terms of computational effort and energy consumption. At the same time, emerging Near-Data Processing (NDP) architectures promise drastic time and energy cuts for applications with data streaming behavior. In this paper, a speculative Motion Compensation (MC) is proposed to enable video decoders improvements through the exploitation of NDP. We adopted a large-vector SIMD-based NDP system (called VIMA) that provides high-performance operations over 2 K vectors. The proposed strategy leverages the correlation between the prediction modes and the motion data between spatially neighboring blocks within a frame to speculatively perform the MC for an entire region of 2Kx128 samples. MC interpolation kernels were implemented using VIMA and x86 AVX-256 SIMD libraries. Our NDP-based kernel implementation allows speedup of $1.9\times$ to $22\times$ compared to the x86 baseline solutions. Stepping forward, based on a coalescence estimation, our strategy can properly handle interpolation misses, achieving MC performance improvements from 7% to 64%.