A deep learning object tracking accelerator

Since the current nerual network accelerator couldn't efficiently accelerate the post-processing of object tracking, a dedicated object trackeris proposed. A neural network architecture is introduced to extract the features of the input feature map. At the meanwhile, it generates thebounding box confidence and position offset sets. Adedicated acceleration module is designed for the anchor regression, penalty calculation and extraction.By synchronizing the data between the neural network accelerator and the dedicated module, a new pipelined structure is proposed to execute the feature extraction and anchor regression in parallel. Therefore, the end-to-end processing of the object tracking is efficiently achieved. The accelerator consumes an area of 3.64 mm² under the SMIC 40nm process, and achieves 5.71 Tops/W energy efficiency. Experimental results show that, compared with the current accleration solutions, the object tracking accelerator achieves 1.53 times acceleration, and it could realize real-time video processing(31fps). For the post-processing task of the tracking, the processing speeds of the proposed dedicated module is improved by 3.2 times than the RISC processor.