A simultaneous multithreading heterogeneous object recognition processor with machine learning based dynamic resource management

A simultaneous multithreading multicore processor is proposed to accelerate object recognition for 720p HD video streams. The multithreading architecture with Q-learning based dynamic resource management enables concurrent processing of 8 region-of-interests with 5-stage fine grained recognition pipeline outperforming previous object recognition processors with 342GOPS computing power. In addition, the dynamic resource management contributes to increase of energy efficiency by applying the on-line learning DVFS and dynamic tile allocation based on task variance and hardware utilization to achieve 9.6mJ/frame with 1280×720 pixel image. It achieves 2.72× throughput and 3.7× energy efficiency compared to previous recognition processors.