RED: Adaptive Real-Time DAG Scheduling for Robotic Inference under Environmental Dynamics

Engineering notes

RED is implemented on NVIDIA Jetson and Apple M-series platforms, showing significant improvements in throughput and deadline satisfaction. It is highly relevant for engineers working on resource-constrained robotics in dynamic environments.

Chinese explanation / 中文解读

本论文提出了一种名为RED的实时调度框架，用于应对动态环境下机器人推理中的计算变化。该框架通过动态调整任务优先级，确保在资源有限的条件下多任务深度神经网络工作负载的实时执行，并提高了兼容性和效率。

Original abstract

Robots deployed in dynamic environments must contend with environment-driven changes that reshape computation at runtime: new tasks may appear, precedence relations can shift, and overall workload structure evolves, all of which degrade performance, especially when multi-task inference is required under tight resource and real-time budgets. We present RED, a real-time scheduling framework for multi-task deep neural network workloads on resource-constrained robotic platforms that adapts to Robotic Environmental Dynamics (RED) while preserving end-to-end timing guarantees under modeling assumptions. The core of RED is a deadline-aware scheduler that assigns intermediate sub-deadlines, allowing it to accommodate evolving computation graphs and asynchronous inference induced by unpredictable conditions. The framework also supports flexible deployment of MIMONet (multi-input multi-output neural networks), commonly used in multi-tasking robots to alleviate memory pressure through weight sharing. RED explicitly leverages this shared-parameter property via a workload refinement and graph-reconstruction procedure that aligns MIMONet structure with schedulability requirements, improving compatibility and efficiency. We implement RED on NVIDIA Jetson family platforms and on an Apple M-series MacBook and evaluate it on navigation-oriented workloads representative of real robotic scenarios. Experiments show consistent gains over existing methods in throughput, deadline satisfaction, robustness to interference, adaptability, and runtime overhead.

Links and sources

• Official / arXiv page
• PDF from original source

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