A Precision-Scalable Vision Accelerator for Robotic Applications

Robot vision systems, by providing abundant and crucial environmental information, enable robots to intelligently perceive environment and make autonomous decisions. However, DNN-based models targeting the depth estimation task as well as other robotic applications tend to be computationally complex, bringing challenges to the efficient deployment on edge devices. In this paper, we propose a precision-scalable vision accelerator for robotic applications. Firstly, we develop a bit-level computing strategy to build the fundamental processing unit for precision scalability, reducing hardware complexity dramatically. Secondly, we present an efficient processing unit group with optimizations in parallelism scalability and overhead reduction. Thirdly, an energy-efficient architecture and the dataflow are proposed, enabling the accelerator to flexibly handle various operations in visual tasks targeting robotic applications like depth estimation. Our design is synthesized under TSMC 28nm CMOS technology. Experiments show that our design achieves a 6.05 TOPS/W energy efficiency as well as 2.23× and 2.13× area efficiency compared with the previous precision-scalable accelerators and the stereo vision accelerators respectively.