RVCar: An FPGA-Based Simple and Open-Source Mini Motor Car System with a RISC-V Soft Processor

Deep Neural Network (DNN) is widely used for computer vision tasks, such as image classification, object detection, and segmenta-tion. DNN accelerator on FPGA and especially Convolutional Neural Net-work (CNN) is a hot topic. More research and education should be con-ducted to boost this field. A starting point is required to make it easy for new entrants to join this field. We believe that FPGA-based Autonomous Driving (AD) motor cars are suitable for this because DNN accelerators can be used for image processing with low latency. In this paper, we propose an FPGA-based simple and open-source mini motor car system named RVCar with a RISC-V soft processor and a CNN accelerator. RVCar is suitable for the new entrants who want to learn the implementation of a CNN acceler-ator and the surrounding system. The motor car consists of Xilinx Nexys A7 board and simple parts. All modules except the CNN accelerator are implemented in Verilog HDL and SystemVerilog. The CNN accelerator is converted from a PyTorch model by our tool. The accelerator is written in C++, synthesizable by Vitis HLS, and an easy-to-customize baseline for the new entrants. FreeRTOS is used to implement AD algorithms and ex-ecuted on the RISC-V soft processor. It helps the users to develop the AD algorithms efficiently. We conduct a case study of the simple AD task we define. Although the task is simple, it is difficult to achieve without im-age recognition. We confirm that RVCar can recognize objects and make correct decisions based on the results.