Accelerating autonomous vehicles: Harnessing FPGA power for deep learning advancements

Abstract

In the rapidly developing field of autonomous vehicles (AV), the integration of deep learning algorithms has become the cornerstone for improving vehicle perception and decision-making ability. This study investigates the potential of field-programmable gate arrays (FPGAs) as hardware accelerators for deep-learning tasks in self-driving cars. This study used a Zynq UltraScale+ MPSoC FPGA board from Xilinx, known for its performance and adaptability, and combined it with a high-resolution camera to simulate real-world visual data encountered by AVs. The approach involved implementing a convolutional neural network (CNN) to perform tasks such as object detection, lane detection, and traffic sign classification. The model was quantized and converted to VHDL code using Xilinx Vivado HLS to optimize the deep learning algorithm for FPGA.The results show that the FPGA-based model significantly outperforms the traditional CPU-based model in terms of processing speed and energy efficiency without sacrificing accuracy. This study highlights the critical role of FPGAs in supporting deep learning tasks in self-driving cars and paves the way for safer and more efficient transportation solutions in the future.