Multi-ASIP based parallel and scalable implementation of motion estimation kernel for high definition videos

Abstract

Parallel implementations of motion estimation for high definition videos typically exploit various forms of parallelism (GOP, frame-, slice- and macroblock-level) to deliver real-time throughput. Although parallel implementations deliver real-time throughput, they often suffer from limited flexibility and scalability due to the form of parallelism and architecture used. In this work, we use Group Of MacroBlocks (GOMB) and Intra-MB (IMB) parallelism with a multi-ASIP (Application Specific Instruction set Processor) architecture to provide a flexible and scalable platform for motion estimation of high definition videos. Multiple GOMBs are processed by the ASIPs in parallel (GOMB-level) where each ASIP is equipped with custom instructions to process the pixels of an MB in parallel (IMB-level). The system is flexible and scalable as the number of ASIPs (number of GOMBs) and custom instructions are not fixed, and are determined through design space exploration. We evaluated the multi-ASIP architecture in Tensilica's commercial design environment with varying number of ASIPs (up to nine), and compared hand-coded and automatically generated custom instructions. The results illustrate that systems with three and seven ASIPs delivered real-time throughput of 30 and 60 fps respectively for “pedestrian”, “rush hour” and “tractor” HD1080p video sequences. In addition, the results indicate that the multi-ASIP platform can be extended for even higher resolutions such as Ultra High Definition (UHD) due to its flexibility and scalability.