Enabling Mixed criticality applications for the Versal AI-Engines

2026-04-22

AI Summary

This work addresses the challenge that static dataflow graph mapping on Versal AI-Engines struggles to support dynamic task scheduling in mixed-criticality systems. To overcome this limitation, the paper introduces, for the first time, a runtime dynamic task dispatch mechanism that enables heterogeneous tasks to be dynamically mapped onto the AI-Engine array through criticality-mode switching, context management, and controlled data copying. This approach simultaneously guarantees real-time performance and improves resource utilization. Experimental results under autonomous driving workloads demonstrate a 65.5% reduction in AI-Engine idle time, a twofold increase in throughput for low-criticality tasks, and scheduling overhead below 0.002% of total execution time.

Technology Category

• Machine Learning: Hardware-aware ML
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Adaptive Systems-on-Chips (SoCs) are increasingly being used in mixed criticality systems (MCSs), such as in autonomous driving, aviation and medical systems. In this context, AMD has proposed the Versal SoC, which has a heterogeneous architecture including, among other components, an Artificial Intelligence Engine (AIE), which is a 2D array of processors and memory tiles designed for AI and signal processing workloads. While this AIE offers significant potential for accelerating real-time data processing tasks, this has not yet been explored in the context of MCSs since individual tasks with different criticality levels cannot be dynamically assigned to tiles due to the static mapping of dataflow graphs and tasks. In this work, we propose a dynamic task dispatching infrastructure that enables task switching on the AIE at runtime. Based on this infrastructure, we present an MCS design that dynamically assigns tasks of different criticality to a pool of AIE tiles, depending on the criticality mode of the system. Our approach overcomes the limitations of static dataflow graph mappings and, for the first time, exploits the parallel processing capabilities of the AIE for MCSs. We also present a comprehensive timing analysis of the overhead introduced by the task dispatcher infrastructure, focusing on control logic, context switching and data copy operations. This shows that these operations have low variance and are negligible compared to the overall execution time, demonstrating that our infrastructure is suitable for MCSs. Finally, we evaluate the proposed infrastructure using an autonomous driving workload with tasks that have variable execution times and different criticality levels. In this case study, we maximized AIE utilization, reducing idle time by 65.5 %, while measuring an execution time overhead of less than 0.002 %, and doubling the throughput of low-criticality tasks.

Problem

• Mixed Criticality Systems
• AI Engine
• Dynamic Task Assignment
• Static Dataflow Mapping
• Versal SoC

Innovation

• dynamic task dispatching
• mixed criticality systems
• AI Engine (AIE)
• runtime reconfiguration
• heterogeneous SoC