Real-Time Semantic Segmentation on FPGA for Autonomous Vehicles Using LMIINet with the CGRA4ML Framework

TL;DR

This paper presents an FPGA-based real-time semantic segmentation system using a lightweight neural network and the CGRA4ML framework, achieving high accuracy and low latency suitable for autonomous vehicles.

Contribution

It introduces an FPGA implementation of semantic segmentation with model modifications for hardware constraints, demonstrating real-time performance and efficiency.

Findings

Achieves 90% pixel accuracy and 45% mIoU.

Operates at 20 FPS with 50.1 ms latency on FPGA.

Outperforms traditional GPU solutions in power efficiency.

Abstract

Semantic segmentation has emerged as a fundamental problem in computer vision, gaining particular importance in real-time applications such as autonomous driving. The main challenge is achieving high accuracy while operating under computational and hardware constraints. In this research, we present an FPGA-based implementation of real-time semantic segmentation leveraging the lightweight LMIINet architecture and the Coarse-Grained Reconfigurable Array for Machine Learning (CGRA4ML) hardware framework. The model was trained using Quantization-Aware Training (QAT) with 8-bit precision on the Cityscapes dataset, reducing memory footprint by a factor of four while enabling efficient fixed-point computations. Necessary modifications were applied to adapt the model to CGRA4ML constraints, including simplifying skip connections, employing hardware-friendly operations such as depthwise-separable and 1A-1 convolutions, and redesigning parts of the Flatten Transformer. Our implementation achieves approximately 90% pixel accuracy and 45% mean Intersection-over-Union (mIoU), operating in real-time at 20 frames per second (FPS) with 50.1 ms latency on the ZCU104 FPGA board. The results demonstrate the potential of CGRA4ML, with its flexibility in mapping modern layers and off-chip memory utilization for skip connections, provides a path for implementing advanced semantic segmentation networks on FPGA for real-time applications to outperform traditional GPU solutions in terms of power efficiency while maintaining competitive accuracy. The code for this project is publicly available at https://github.com/STAmirr/ cgra4ml_semantic_segmentation