Performance Optimisation of Parallelized ADAS Applications in FPGA-GPU Heterogeneous Systems: A Case Study With Lane Detection

Abstract

The explosive growth of massive data captured by various sensors on modern vehicles has impelled the deployment of Commercial Off-The-Shelf (COTS) accelerators for the research and development of Advanced Driver Assistance Systems (ADAS). Although the advent of cross-platform programming framework such as Open Computing Language (OpenCL) facilitates the programmability of ADAS applications on heterogeneous devices, the performance portability is still vulnerable and subject to different hardware implementations by the heterogeneous manufacturers. With this issue in mind, in this article we propose a detailed procedure that helps guide the performance optimisation of parallelized ADAS applications in an FPGA-GPU combined heterogeneous system. Taking two different lane detection applications as case studies, we provide one intra-accelerator and two interaccelerator optimisation methods, as well as both FPGA-specific and application-oriented optimisation strategies, to boost the program runtime performance. Experiment results on a heterogeneous platform with COTS FPGA and GPU components reveal that the optimal designs generated from the procedure can improve the runtime performance of the two applications by an average of 109.21% and 83.48% over the native parallel implementations, respectively.

An Intelligent Task Scheduling Mechanism for Autonomous Vehicles via Deep Learning

“…The proposed algorithms are validated using an estimated few scheduling metrics such as task miss rate, execution time (makespan), cluster utilization, and prediction accuracy were estimated as per the below equations. φ = µ ρ × 100 (11) where φ accuracy rate of core prediction µ denotes the predicted number of cores divided by ρ an actual number of active cores.…”

“…Similarly, Xiebang Wang et al adopted the HMPSoC computing system with openCL software kernels for Advance Driver-Assistance System (ADAS) operations [11]. Tosiron et al studied the role of multicore processor optimization in IoT systems.…”

QuantLaneNet: A 640-FPS and 34-GOPS/W FPGA-Based CNN Accelerator for Lane Detection

“…Kojima et al [ 21 ] presents an autonomous driving system consisting of lane-keeping, localization, driving planning, and obstacle avoidance that are implemented as software in the embedded processor on FPGA. Wang et al [ 22 ] propose a detailed procedure that helps guide the performance optimization of parallelized ADAS applications in an FPGA-Graphics Processing Unit (GPU) combined heterogeneous system. Kamimae et al [ 23 ] develop an SoC FPGA based on the Helmholtz Principle to control unmanned mobile vehicles for the FPGA design competition.…”