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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. 

Vehicle tracking, a core application to smart city video analytics, is becoming more widely deployed than ever before thanks to the increasing number of traffic cameras and recent advances in computer vision and machine-learning. Due to the constraints of bandwidth, latency, and privacy concerns, tracking tasks are more preferable to run on edge devices sitting close to the cameras. However, edge devices are provisioned with a fixed amount of computing budget, making them incompetent to adapt to time-varying and imbalanced tracking workloads caused by traffic dynamics. In coping with this challenge, we propose WatchDog, a real-time vehicle tracking system that fully utilizes edge nodes across the road network. WatchDog leverages computer vision tasks with different resource-accuracy tradeoffs, and decomposes and schedules tracking tasks judiciously across edge devices based on the current workload to maximize the number of tasks while ensuring a provable response time-bound at each edge device. Extensive evaluations have been conducted using real-world city-wide vehicle trajectory datasets, achieving exceptional tracking performance with a real-time guarantee.