YOLO real-time object detection on EV3-Robot using FPGA hardware Accelerator
Dinda Pramanta, Ninnart Fuengfusin, Arie Rachmad Syulistyo, Hakaru Tamukoh · Proceedings of International Conference on Artificial Life and Robotics · 2024
The growing demand for robots necessitates faster and more precise processing.However, running large Artificial Intelligence (AI) models from cloud data centers to mobile robots via inference models uses considerable computation resources, which leads to power limitations, particularly for mobile robots.The use of reconfigurable semiconductor devices at the hardware level is a promising solution to this problem.We introduce the educational kit EV3-Robot with a co-design methodology utilizing Field-programmable Gate Arrays (FPGA) Kria KV260 as a hardware accelerator specifically for object detection.We apply the You Only Look Once (YOLO) model for object detection, which provides real-time results for practical applications.Additionally, we analyze the processing times of the local PC and EV3-Robot.