arXiv subjects

Daniel Kuhse

Publications and source records attributed to Daniel Kuhse.

3 recordsLinked to original sources

arXiv2026

Deductive Verification for Earliest Deadline First Scheduler Implementations

Real-Time Operating Systems (RTOSes) rely on scheduler implementations to provide predictable task execution. For safety-critical systems, it is therefore not sufficient to reason only about the abstract scheduling policy; the concrete implementation must also preserve the intended scheduling semantics. This is particularly challenging for Earliest Deadline First (EDF) scheduling, because EDF introduces dynamic, deadline-derived priorities that are often realized by reusing kernel infrastructure originally designed for fixed-priority scheduling. In this work, we formalize EDF correctness through three essential properties that any implementation of the Earliest Deadline First (EDF) scheduler must satisfy. Based on these properties, we propose a framework utilizing deductive verification, that applies to any EDF-based scheduler realization. We instantiate the framework in Frama-C/ACSL and apply it to three structurally different EDF scheduler realizations: RTEMS 5, RTEMS 6, and an EDF extension of FreeRTOS.

cs.OS↗

arXiv2026

Jump Like A Squirrel: Optimized Execution Step Order for Anytime Random Forest Inference

Due to their efficiency and small size, decision trees and random forests are popular machine learning models used for classification on resource-constrained systems. In such systems, the available execution time for inference in a random forest might not be sufficient for a complete model execution. Ideally, the already gained prediction confidence should be retained. An anytime algorithm is designed to be able to be aborted anytime, while giving a result with an increasing quality over time. Previous approaches have realized random forests as anytime algorithms on the granularity of trees, stopping after some but not all trees of a forest have been executed. However, due to the way decision trees subdivide the sample space in every step, an increase in prediction quality is achieved with every additional step in one tree. In this paper, we realize decision trees and random forest as anytime algorithms on the granularity of single steps in trees. This approach opens a design space to define the step order in a forest, which has the potential to optimize the mean accuracy. We propose the Optimal Order, which finds a step order with a maximal mean accuracy in exponential runtime and the polynomial runtime heuristics Forward Squirrel Order and Backward Squirrel Order, which greedily maximize the accuracy for each additional step taken down and up the trees, respectively. Our evaluation shows, that the Backward Squirrel Order performs $\sim94\%$ as well as the Optimal Order and $\sim99\%$ as well as all other step orders.

cs.LG↗

arXiv2025

You Only Look Once at Anytime (AnytimeYOLO): Analysis and Optimization of Early-Exits for Object-Detection

We introduce AnytimeYOLO, a family of variants of the YOLO architecture that enables anytime object detection. Our AnytimeYOLO networks allow for interruptible inference, i.e., they provide a prediction at any point in time, a property desirable for safety-critical real-time applications. We present structured explorations to modify the YOLO architecture, enabling early termination to obtain intermediate results. We focus on providing fine-grained control through high granularity of available termination points. First, we formalize Anytime Models as a special class of prediction models that offer anytime predictions. Then, we discuss a novel transposed variant of the YOLO architecture, that changes the architecture to enable better early predictions and greater freedom for the order of processing stages. Finally, we propose two optimization algorithms that, given an anytime model, can be used to determine the optimal exit execution order and the optimal subset of early-exits to select for deployment in low-resource environments. We evaluate the anytime performance and trade-offs of design choices, proposing a new anytime quality metric for this purpose. In particular, we also discuss key challenges for anytime inference that currently make its deployment costly.

cs.CV↗