Data Partition Optimization for High Energy Efficiency by Decoupling Local Dependence in Holographic Video Decoder

Holography has attracted considerable attention from researchers due to its ability to store and recreate the wavefront emanating from a three-dimensional object. However, holographic video requires enormous resolution (128 k × 128 k) at the same frame rates(60fps) as normal video to achieve acceptable visual effects. Data compression is thus essential for its storage/transmission. When implementing its decompression pipeline on hardware for mobile scenarios, data dependency and energy consumption must be handled carefully. In this work, we present a novel design framework and a data partition optimization approach to optimize the overall energy consumption by tackling local dependence in the motion compensation module for holographic video codec, and exploring the design space of data partition layout. First, we propose a local data dependency propagation (LDDP) method that transforms one holographic frame with strong local dependence into multiple mutually independent virtual blocks without local dependence at all. Second, we formulate a model for the data partitioning problem, allowing us to analyze and optimize energy consumption by adjusting the layout of data partitions. Third, we provide a heuristic and efficient solution to the formulated model taking advantage of the target application scenarios. Experiment results in various scenarios show that our proposed optimization method achieves 2.94 3.91 × energy efficiency and 46.37% 63.63% area efficiency compared to baseline approaches.