A Low Cost and Portable Mini Motor Car System with a BNN Accelerator on FPGA

Abstract

To realize autonomous driving, a deep neural network (DNN) is one of the key technologies. However, since DNN needs a lot of computation, it is challenging for an edge device to support DNN with limited computation resources. A binarized neural network (BNN) has been proposed to reduce latency and parameter size and is suited for hardware implementation. Since current DNN technology is a growing and better algorithm change with time, implementing DNN on an FPGA is preferable to an ASIC. In this paper, we propose a low cost and portable mini motor car system with a BNN accelerator on an FPGA. We compare the road tracking demonstration with a similar motor car using Raspberry Pi and show the effectiveness of FPGA in a DNN implementation. The proposed system is implemented on Nexys A7, one of the most popular FPGA development boards using an Artix-7 FPGA.