Fast DNN-based Mechatronics Prototyping Platform on Robotic Arm Control

Yu-Chien Chung, Hao-Hsiang Lian, Yong-Lun Xiao, Chih-Tsun Huang, Jing-Jia Liou

Abstract: In industrial applications, a robotic controller re-quires a low-latency computation process for real-time con-straints. In the meantime, more controllers are designed with DNN-based reinforcement learning, which needs increasing computation power. In this demo, we developed a fast prototyping infrastructure in AI -based mechatronics. Our software/hardware co-optimization incorporates a cyber-physical system (CPS), a host computer, and a DNN-based accelerator on an FPGA. The holistic accelerator is built upon the ESP SoC (System-on-Chip) platform with the high-level synthesis (HLS) technique and an improved interface. Our demonstration on an intelligent robotic arm showcases 101 times speedup over a CPU-based software implementation.