RED: Adaptive Real-Time DAG Scheduling for Robotic Inference under Environmental Dynamics
RED is a real-time scheduling framework for multi-task deep neural network workloads on resource-constrained robotic platforms. It adapts to runtime environmental changes by assigning intermediate sub-deadlines, leveraging MIMONet weight sharing, and reconstructing computation graphs. Implemented on NVIDIA Jetson and Apple M-series platforms, RED consistently outperforms existing methods in throughput, deadline satisfaction, robustness, adaptability, and overhead.
Article intelligence
Key points
- RED assigns intermediate sub-deadlines to accommodate evolving computation graphs and asynchronous inference.
- It leverages MIMONet's shared parameters to improve schedulability through workload refinement and graph reconstruction.
- Experiments on NVIDIA Jetson and Apple M-series hardware show consistent gains over state-of-the-art methods in multiple metrics.
Why it matters
This matters because RED assigns intermediate sub-deadlines to accommodate evolving computation graphs and asynchronous inference.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.24044] RED: Adaptive Real-Time DAG Scheduling for Robotic Inference under Environmental Dynamics
[Submitted on 21 May 2026]
Title:RED: Adaptive Real-Time DAG Scheduling for Robotic Inference under Environmental Dynamics
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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.
Comments: Extension version of RTSS'23
Subjects:
Robotics (cs.RO); Software Engineering (cs.SE); Systems and Control (eess.SY)
Cite as: arXiv:2605.24044 [cs.RO]
(or arXiv:2605.24044v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.24044
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zexin Li [view email] [v1] Thu, 21 May 2026 20:44:39 UTC (2,468 KB)
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