Development of an Energy-Efficient and Real-Time Data Movement Strategy for Next-Generation Heterogeneous Mixed-Criticality Systems

2026-02-10

AI Summary

This work addresses the challenges of real-time performance, energy efficiency, and resource contention among multi-criticality tasks in heterogeneous mixed-criticality systems for applications such as autonomous driving and robotics. The paper proposes a hardware-software co-designed data movement strategy that, for the first time, tightly integrates mixed-criticality assurance mechanisms with energy-efficient data transmission. By leveraging heterogeneous architecture modeling, multi-criticality-aware scheduling, co-design of interconnect and memory subsystems, and application-aware dataflow management, the approach enables low-interference, highly predictable communication across criticality levels. Evaluated under representative ACES scenarios, the system demonstrates significantly improved energy efficiency, substantially reduced interference on high-priority tasks, and enhanced predictability of critical-task latency.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Hardware-aware ML
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Industrial domains such as automotive, robotics, and aerospace are rapidly evolving to satisfy the increasing demand for machine-learning-driven Autonomy, Connectivity, Electrification, and Shared mobility (ACES). This paradigm shift inherently and significantly increases the requirement for onboard computing performance and high-performance communication infrastructure. At the same time, Moore's Law and Dennard Scaling are grinding to a halt, in turn, driving computing systems to larger scales and higher levels of heterogeneity and specialization, through application-specific hardware accelerators, instead of relying on technological scaling only. Approaching ACES requires this substantial amount of compute at an increasingly high energy-efficiency, since most use cases are fundamentally resource-bound. This increase in compute performance and heterogeneity goes hand in hand with a growing demand for high memory bandwidth and capacity as the driving applications grow in complexity, operating on huge and progressively irregular data sets and further requiring a steady influx of sensor data, increasing pressure both on on-chip and off-chip interconnect systems. Further, ACES combines real-time time-critical with general compute tasks on the same physical platform, sharing communication, storage, and micro-architectural resources. These heterogeneous mixed-criticality systems (MCSs) place additional pressure on the interconnect, demanding minimal contention between the different criticality levels to sustain a high degree of predictability. Fulfilling the performance and energy-efficiency requirements across a wide range of industrial applications requires a carefully co-designed process of the memory system with the use cases as well as the compute units and accelerators.

Problem

• mixed-criticality systems
• energy-efficiency
• real-time
• heterogeneous computing
• data movement

Innovation

• heterogeneous mixed-criticality systems
• energy-efficient data movement
• real-time communication
• hardware-software co-design
• memory-system co-optimization