arXiv subjects

Nils Hölscher

Publications and source records attributed to Nils Hölscher.

3 recordsLinked to original sources

arXiv2026

LLTA: A Simplicity-Oriented Open-Source WCET Analyser

Deriving a safe upper bound on the worst-case execution time (WCET) of a real-time task is essential for hard real-time systems. Many WCET analysers exist, but they are either (i) closed source or (ii) do not provide a WCET for an existing microcontroller. Hence, it is difficult to obtain a trustworthy WCET bound for the binary that is flashed to an available embedded hardware platform and to observe whether the bound is safe. To solve this, we present LLTA, an open-source WCET analyser for Commercial-Off-The-Shelf (COTS) microcontrollers. LLTA is designed based on the following goals: it is simple (G1), runs on real COTS hardware (G2), reports results that are unsound due to programming or device constraints (G3), and is available open-source without licensing complications (G4). LLTA generates WCETs for the ESP32-C6 and the MSP430(FR5994), which are broadly available. This paper systematically lays out our design decisions for LLTA with those goals in mind. To demonstrate its ease of use, we propose scenarios where LLTA can be used for lab exercises of real-time systems.

cs.PF↗

arXiv2025

Modeling and Simulating Emerging Memory Technologies: A Tutorial

Non-volatile Memory (NVM) technologies present a promising alternative to traditional volatile memories such as SRAM and DRAM. Due to the limited availability of real NVM devices, simulators play a crucial role in architectural exploration and hardware-software co-design. This tutorial presents a simulation toolchain through four detailed case studies, showcasing its applicability to various domains of system design, including hybrid main-memory and cache, compute-in-memory, and wear-leveling design. These case studies provide the reader with practical insights on customizing the toolchain for their specific research needs. The source code is open-sourced.

cs.AR↗

arXiv2025

WCDT: Systematic WCET Optimization for Decision Tree Implementations

Machine-learning models are increasingly deployed on resource-constrained embedded systems with strict timing constraints. In such scenarios, the worst-case execution time (WCET) of the models is required to ensure safe operation. Specifically, decision trees are a prominent class of machine-learning models and the main building blocks of tree-based ensemble models (e.g., random forests), which are commonly employed in resource-constrained embedded systems. In this paper, we develop a systematic approach for WCET optimization of decision tree implementations. To this end, we introduce a linear surrogate model that estimates the execution time of individual paths through a decision tree based on the path's length and the number of taken branches. We provide an optimization algorithm that constructively builds a WCET-optimal implementation of a given decision tree with respect to this surrogate model. We experimentally evaluate both the surrogate model and the WCET-optimization algorithm. The evaluation shows that the optimization algorithm improves analytically determined WCET by up to $17\%$ compared to an unoptimized implementation.

cs.LG↗