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Real-time systems are widely applied in different areas like autonomous vehicles, where safety is the key metric. However, on the FPGA platform, most of the prior accelerator frameworks omit discussing the schedulability in such real-time safety-critical systems, leaving deadlines unmet, which can lead to catastrophic system failures. To address this, we propose the ART framework, a hardware-software co-design approach that transforms baseline accelerators into “real-time guaranteed" accelerators. On the software side, ART performs schedulability analysis and preemption point placement, optimizing task scheduling to meet deadlines and enhance throughput. On the hardware side, ART integrates the Global Earliest Deadline First (GEDF) scheduling algorithm, implements preemption, and conducts source code transformation to transform baseline HLS-based accelerators into designs targeted for real-time systems capable of saving and resuming tasks. ART also includes integration, debugging, and testing tools for full-system implementation. We demonstrate the methodology of ART on two kinds of popular accelerator models and evaluate on AMD Versal VCK190 platform, where ART meets schedulability requirements that baseline accelerators fail. ART is lightweight, utilizing <0.5% resources. With about 100 lines of user input, ART generates about 2.5k lines of accelerator code, making it a push-button solution. 

In the realm of connected autonomous vehicles, the integration of data from both onboard and edge sensors is vital for environmental perception and navigation. However, the fusion of this sensor data faces challenges due to timestamp disparities, particularly when edge devices are involved. The Robotic Operating System (ROS) addresses this with synchronization policies like Approximate and Exact Time, along with the newer Synchronizing the Earliest Arrival Messages (SEAM). Understanding SEAM’s performance in edge-assisted environments is crucial yet under-explored. This paper presents a comprehensive analysis of SEAM synchronization within ROS. Our study focuses on critical latency metrics for ROS message synchronization in edge-assisted autonomous driving. Specifically, we analyze two key latency metrics, the passing latency and reaction latency, which are needed to analyze the end-to-end delay and reaction time on the system level. We conduct experiments under different settings to evaluate the precision of our proposed latency upper bounds against the maximum experimental latency in simulation.