11 Search Results for "Reghenzani, Federico"

Volume

OASIcs, Volume 98

Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022)

NG-RES 2022, June 22, 2022, Budapest, Hungary

Editors: Marko Bertogna, Federico Terraneo, and Federico Reghenzani

Document
DOI: 10.4230/LITES.11.1.1
A Survey of Real-Time Support, Analysis, and Advancements in ROS 2

Authors: Daniel Casini, Jian-Jia Chen, Jing Li, Federico Reghenzani, and Harun Teper

Published in: LITES, Volume 11, Issue 1 (2026). Leibniz Transactions on Embedded Systems, Volume 11, Issue 1

Abstract
The Robot Operating System 2 (ROS 2) has emerged as a relevant middleware framework for robotic applications, offering modularity, distributed execution, and communication. In the last six years, ROS 2 has drawn increasing attention from the real-time systems community and industry. This survey presents a comprehensive overview of research efforts that analyze, enhance, and extend ROS 2 to support real-time execution. We first provide a detailed description of the internal scheduling mechanisms of ROS 2 and its layered architecture, including the interaction with DDS-based communication and other communication middleware. We then review key contributions from the literature, covering timing analysis for both single- and multi-threaded executors, metrics such as response time, reaction time, and data age, and different communication modes. The survey also discusses community-driven enhancements to the ROS 2 runtime, including new executor algorithm designs, real-time GPU management, and microcontroller support via micro-ROS. Furthermore, we summarize techniques for bounding DDS communication delays, message filters, and profiling tools that have been developed to support analysis and experimentation. To help systematize this growing body of work, we introduce taxonomies that classify the surveyed contributions based on different criteria. This survey aims to guide both researchers and practitioners in understanding and improving the real-time capabilities of ROS 2.
Cite as

Daniel Casini, Jian-Jia Chen, Jing Li, Federico Reghenzani, and Harun Teper. A Survey of Real-Time Support, Analysis, and Advancements in ROS 2. In LITES, Volume 11, Issue 1 (2026). Leibniz Transactions on Embedded Systems, Volume 11, Issue 1, pp. 1:1-1:37, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

Copy BibTex To Clipboard

@Article{casini_et_al:LITES.11.1.1,
  author =	{Casini, Daniel and Chen, Jian-Jia and Li, Jing and Reghenzani, Federico and Teper, Harun},
  title =	{{A Survey of Real-Time Support, Analysis, and Advancements in ROS 2}},
  journal =	{Leibniz Transactions on Embedded Systems},
  pages =	{1:1--1:37},
  ISSN =	{2199-2002},
  year =	{2026},
  volume =	{11},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LITES.11.1.1},
  URN =		{urn:nbn:de:0030-drops-257914},
  doi =		{10.4230/LITES.11.1.1},
  annote =	{Keywords: ROS 2, middleware, real-time, timing predictability, publish-subscribe}
}
Document
DOI: 10.4230/LIPIcs.ECRTS.2025.20
Detecting Low-Density Mixtures in High-Quantile Tails for pWCET Estimation

Authors: Blau Manau, Sergi Vilardell, Isabel Serra, Enrico Mezzetti, Jaume Abella, and Francisco J. Cazorla

Published in: LIPIcs, Volume 335, 37th Euromicro Conference on Real-Time Systems (ECRTS 2025)

Abstract
The variability arising from sophisticated hardware and software solutions in cutting-edge embedded products causes software to exhibit complex execution time distributions. Mixture distributions can happen, with different density (weight), as a result of inherent different features in the execution platform and multiple operational scenarios. In the context of probabilistic WCET (pWCET) analysis based on Extreme Value Theory (EVT), where identical distribution is a pre-requisite, mixtures are typically intercepted by applying stationarity tests on the full sample. Those tests, however, are instructed to detect only mixtures with sufficiently high probability (weight) and disregard low-density mixtures (which are unlikely to be preserved in the high-quantile tail of the sample) as they would prevent any form of stationarity. Nonetheless, low-density mixture distributions can persist and even exacerbate in the tail, and, when not considered, they can impair pWCET estimation in EVT-based approaches, leading to overly pessimistic or optimistic bounds. In this work, we propose TailID, an iterative point-wise approach that builds on the asymptotic convergence of the Maximum Likelihood Estimator (MLE) of the Extreme Value Index (EVI) parameter ξ to detect low-density mixture distributions on high-quantile tails and use this information to steer EVT tail selection. The benefits of the proposed method are assessed on synthetic mixture distributions and real data collected on an industrially representative embedded platform.
Cite as

Blau Manau, Sergi Vilardell, Isabel Serra, Enrico Mezzetti, Jaume Abella, and Francisco J. Cazorla. Detecting Low-Density Mixtures in High-Quantile Tails for pWCET Estimation. In 37th Euromicro Conference on Real-Time Systems (ECRTS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 335, pp. 20:1-20:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{manau_et_al:LIPIcs.ECRTS.2025.20,
  author =	{Manau, Blau and Vilardell, Sergi and Serra, Isabel and Mezzetti, Enrico and Abella, Jaume and Cazorla, Francisco J.},
  title =	{{Detecting Low-Density Mixtures in High-Quantile Tails for pWCET Estimation}},
  booktitle =	{37th Euromicro Conference on Real-Time Systems (ECRTS 2025)},
  pages =	{20:1--20:25},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-377-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{335},
  editor =	{Mancuso, Renato},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2025.20},
  URN =		{urn:nbn:de:0030-drops-235982},
  doi =		{10.4230/LIPIcs.ECRTS.2025.20},
  annote =	{Keywords: WCET, EVT}
}
Document
DOI: 10.4230/LIPIcs.ECRTS.2025.13
Faster Classification of Time-Series Input Streams

Authors: Kunal Agrawal, Sanjoy Baruah, Zhishan Guo, Jing Li, Federico Reghenzani, Kecheng Yang, and Jinhao Zhao

Published in: LIPIcs, Volume 335, 37th Euromicro Conference on Real-Time Systems (ECRTS 2025)

Abstract
Deep learning–based classifiers are widely used for perception in autonomous Cyber-Physical Systems (CPS’s). However, such classifiers rarely offer guarantees of perfect accuracy while being optimized for efficiency. To support safety-critical perception, ensembles of multiple different classifiers working in concert are typically used. Since CPS’s interact with the physical world continuously, it is not unreasonable to expect dependencies among successive inputs in a stream of sensor data. Prior work introduced a classification technique that leverages these inter-input dependencies to reduce the average time to successful classification using classifier ensembles. In this paper, we propose generalizations to this classification technique, both in the improved generation of classifier cascades and the modeling of temporal dependencies. We demonstrate, through theoretical analysis and numerical evaluation, that our approach achieves further reductions in average classification latency compared to the prior methods.
Cite as

Kunal Agrawal, Sanjoy Baruah, Zhishan Guo, Jing Li, Federico Reghenzani, Kecheng Yang, and Jinhao Zhao. Faster Classification of Time-Series Input Streams. In 37th Euromicro Conference on Real-Time Systems (ECRTS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 335, pp. 13:1-13:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{agrawal_et_al:LIPIcs.ECRTS.2025.13,
  author =	{Agrawal, Kunal and Baruah, Sanjoy and Guo, Zhishan and Li, Jing and Reghenzani, Federico and Yang, Kecheng and Zhao, Jinhao},
  title =	{{Faster Classification of Time-Series Input Streams}},
  booktitle =	{37th Euromicro Conference on Real-Time Systems (ECRTS 2025)},
  pages =	{13:1--13:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-377-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{335},
  editor =	{Mancuso, Renato},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2025.13},
  URN =		{urn:nbn:de:0030-drops-235919},
  doi =		{10.4230/LIPIcs.ECRTS.2025.13},
  annote =	{Keywords: Classification, Deep Learning, Sensor data streams, IDK classifiers}
}
Document
Artifact
DOI: 10.4230/DARTS.11.1.4
Faster Classification of Time-Series Input Streams (Artifact)

Authors: Kunal Agrawal, Sanjoy Baruah, Zhishan Guo, Jing Li, Federico Reghenzani, Kecheng Yang, and Jinhao Zhao

Published in: DARTS, Volume 11, Issue 1, Special Issue of the 37th Euromicro Conference on Real-Time Systems (ECRTS 2025)

Abstract
Deep learning–based classifiers are widely used for perception in autonomous Cyber-Physical Systems (CPS’s). However, such classifiers rarely offer guarantees of perfect accuracy while being optimized for efficiency. To support safety-critical perception, ensembles of multiple different classifiers working in concert are typically used. Since CPS’s interact with the physical world continuously, it is not unreasonable to expect dependencies among successive inputs in a stream of sensor data. Prior work introduced a classification technique that leverages these inter-input dependencies to reduce the average time to successful classification using classifier ensembles. In this paper, we propose generalizations to this classification technique, both in the improved generation of classifier cascades and the modeling of temporal dependencies. We demonstrate, through theoretical analysis and numerical evaluation, that our approach achieves further reductions in average classification latency compared to the prior methods.
Cite as

Kunal Agrawal, Sanjoy Baruah, Zhishan Guo, Jing Li, Federico Reghenzani, Kecheng Yang, and Jinhao Zhao. Faster Classification of Time-Series Input Streams (Artifact). In Special Issue of the 37th Euromicro Conference on Real-Time Systems (ECRTS 2025). Dagstuhl Artifacts Series (DARTS), Volume 11, Issue 1, pp. 4:1-4:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@Article{agrawal_et_al:DARTS.11.1.4,
  author =	{Agrawal, Kunal and Baruah, Sanjoy and Guo, Zhishan and Li, Jing and Reghenzani, Federico and Yang, Kecheng and Zhao, Jinhao},
  title =	{{Faster Classification of Time-Series Input Streams (Artifact)}},
  pages =	{4:1--4:3},
  journal =	{Dagstuhl Artifacts Series},
  ISSN =	{2509-8195},
  year =	{2025},
  volume =	{11},
  number =	{1},
  editor =	{Agrawal, Kunal and Baruah, Sanjoy and Guo, Zhishan and Li, Jing and Reghenzani, Federico and Yang, Kecheng and Zhao, Jinhao},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DARTS.11.1.4},
  URN =		{urn:nbn:de:0030-drops-236057},
  doi =		{10.4230/DARTS.11.1.4},
  annote =	{Keywords: Classification, Deep Learning, Sensor data streams, IDK classifiers}
}
Document
Complete Volume
DOI: 10.4230/OASIcs.NG-RES.2022
OASIcs, Volume 98, NG-RES 2022, Complete Volume

Authors: Marko Bertogna, Federico Terraneo, and Federico Reghenzani

Published in: OASIcs, Volume 98, Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022)

Abstract
OASIcs, Volume 98, NG-RES 2022, Complete Volume
Cite as

Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022). Open Access Series in Informatics (OASIcs), Volume 98, pp. 1-58, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

Copy BibTex To Clipboard

@Proceedings{bertogna_et_al:OASIcs.NG-RES.2022,
  title =	{{OASIcs, Volume 98, NG-RES 2022, Complete Volume}},
  booktitle =	{Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022)},
  pages =	{1--58},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-221-1},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{98},
  editor =	{Bertogna, Marko and Terraneo, Federico and Reghenzani, Federico},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NG-RES.2022},
  URN =		{urn:nbn:de:0030-drops-161070},
  doi =		{10.4230/OASIcs.NG-RES.2022},
  annote =	{Keywords: OASIcs, Volume 98, NG-RES 2022, Complete Volume}
}
Document
Front Matter
DOI: 10.4230/OASIcs.NG-RES.2022.0
Front Matter, Table of Contents, Preface, Conference Organization

Authors: Marko Bertogna, Federico Terraneo, and Federico Reghenzani

Published in: OASIcs, Volume 98, Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022)

Abstract
Front Matter, Table of Contents, Preface, Conference Organization
Cite as

Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022). Open Access Series in Informatics (OASIcs), Volume 98, pp. 0:i-0:x, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

Copy BibTex To Clipboard

@InProceedings{bertogna_et_al:OASIcs.NG-RES.2022.0,
  author =	{Bertogna, Marko and Terraneo, Federico and Reghenzani, Federico},
  title =	{{Front Matter, Table of Contents, Preface, Conference Organization}},
  booktitle =	{Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022)},
  pages =	{0:i--0:x},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-221-1},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{98},
  editor =	{Bertogna, Marko and Terraneo, Federico and Reghenzani, Federico},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NG-RES.2022.0},
  URN =		{urn:nbn:de:0030-drops-161082},
  doi =		{10.4230/OASIcs.NG-RES.2022.0},
  annote =	{Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
Document
Invited Paper
DOI: 10.4230/OASIcs.NG-RES.2022.1
Can We Trust AI-Powered Real-Time Embedded Systems? (Invited Paper)

Authors: Giorgio Buttazzo

Published in: OASIcs, Volume 98, Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022)

Abstract
The excellent performance of deep neural networks and machine learning algorithms is pushing the industry to adopt such a technology in several application domains, including safety-critical ones, as self-driving vehicles, autonomous robots, and diagnosis support systems for medical applications. However, most of the AI methodologies available today have not been designed to work in safety-critical environments and several issues need to be solved, at different architecture levels, to make them trustworthy. This paper presents some of the major problems existing today in AI-powered embedded systems, highlighting possible solutions and research directions to support them, increasing their security, safety, and time predictability.
Cite as

Giorgio Buttazzo. Can We Trust AI-Powered Real-Time Embedded Systems? (Invited Paper). In Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022). Open Access Series in Informatics (OASIcs), Volume 98, pp. 1:1-1:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

Copy BibTex To Clipboard

@InProceedings{buttazzo:OASIcs.NG-RES.2022.1,
  author =	{Buttazzo, Giorgio},
  title =	{{Can We Trust AI-Powered Real-Time Embedded Systems?}},
  booktitle =	{Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022)},
  pages =	{1:1--1:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-221-1},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{98},
  editor =	{Bertogna, Marko and Terraneo, Federico and Reghenzani, Federico},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NG-RES.2022.1},
  URN =		{urn:nbn:de:0030-drops-161099},
  doi =		{10.4230/OASIcs.NG-RES.2022.1},
  annote =	{Keywords: Real-Time Systems, Heterogeneous architectures, Trustworthy AI, Hypervisors, Deep learning, Adversarial attacks, FPGA acceleration, Mixed criticality systems}
}
Document
DOI: 10.4230/OASIcs.NG-RES.2022.2
Multi-Requirement Enforcement of Non-Functional Properties on MPSoCs Using Enforcement FSMs - A Case Study

Authors: Khalil Esper, Stefan Wildermann, and Jürgen Teich

Published in: OASIcs, Volume 98, Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022)

Abstract
Embedded system applications usually have to meet real-time, energy or safety requirements on programs typically concurrently executed on a given MPSoC target platform. Enforcing such properties, e.g., by adapting the number of processors allocated to a program or by scaling the voltage/frequency mode of involved processors, is a difficult problem to solve, especially with a typically large varying environmental input (workload) per execution. In a previous work [Esper et al., 2021], we formalized the related enforcement problem using (a) finite state machines to model enforcement strategies, (b) discrete-time Markov chains to model the uncertain environment determining the system’s workload, and (c) the system response that defines the feedback for the reactive enforcer. In this paper, we apply that approach to specify and verify multi-requirement enforcement strategies and assess a case study for enforcing two independent requirements at the same time, i.e., latency and energy consumption. We evaluate and compare different enforcement strategies using probabilistic verification for the use case of an object detection application.
Cite as

Khalil Esper, Stefan Wildermann, and Jürgen Teich. Multi-Requirement Enforcement of Non-Functional Properties on MPSoCs Using Enforcement FSMs - A Case Study. In Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022). Open Access Series in Informatics (OASIcs), Volume 98, pp. 2:1-2:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

Copy BibTex To Clipboard

@InProceedings{esper_et_al:OASIcs.NG-RES.2022.2,
  author =	{Esper, Khalil and Wildermann, Stefan and Teich, J\"{u}rgen},
  title =	{{Multi-Requirement Enforcement of Non-Functional Properties on MPSoCs Using Enforcement FSMs - A Case Study}},
  booktitle =	{Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022)},
  pages =	{2:1--2:13},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-221-1},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{98},
  editor =	{Bertogna, Marko and Terraneo, Federico and Reghenzani, Federico},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NG-RES.2022.2},
  URN =		{urn:nbn:de:0030-drops-161102},
  doi =		{10.4230/OASIcs.NG-RES.2022.2},
  annote =	{Keywords: Runtime Requirement Enforcement, Verification, Finite State Machine, Markov Chain, Energy Consumption, Probabilistic Model Cheking, PCTL, MPSoC}
}
Document
DOI: 10.4230/OASIcs.NG-RES.2022.3
Overlapping-Horizon MPC: A Novel Approach to Computational Constraints in Real-Time Predictive Control

Authors: Alberto Leva, Simone Formentin, and Silvano Seva

Published in: OASIcs, Volume 98, Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022)

Abstract
Model predictive control (MPC) represents the state of the art technology for multivariable systems subject to hard signal constraints. Nonetheless, in many real-time applications MPC cannot be employed as the minimum acceptable sampling frequency is not compatible with the computational limits of the available hardware, i.e., the optimisation task cannot be accomplished in one sampling period. In this paper we generalise the classical receding-horizon MPC rationale to the case where n > 1 sampling intervals are required to compute the control trajectory. We call our scheme Overlapping-horizon MPC - OH-MPC for short - and we numerically show its attitude at providing a tunable trade-off between optimisation quality and real-time capabilities.
Cite as

Alberto Leva, Simone Formentin, and Silvano Seva. Overlapping-Horizon MPC: A Novel Approach to Computational Constraints in Real-Time Predictive Control. In Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022). Open Access Series in Informatics (OASIcs), Volume 98, pp. 3:1-3:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

Copy BibTex To Clipboard

@InProceedings{leva_et_al:OASIcs.NG-RES.2022.3,
  author =	{Leva, Alberto and Formentin, Simone and Seva, Silvano},
  title =	{{Overlapping-Horizon MPC: A Novel Approach to Computational Constraints in Real-Time Predictive Control}},
  booktitle =	{Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022)},
  pages =	{3:1--3:10},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-221-1},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{98},
  editor =	{Bertogna, Marko and Terraneo, Federico and Reghenzani, Federico},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NG-RES.2022.3},
  URN =		{urn:nbn:de:0030-drops-161118},
  doi =		{10.4230/OASIcs.NG-RES.2022.3},
  annote =	{Keywords: real-time control, model predictive control}
}
Document
DOI: 10.4230/OASIcs.NG-RES.2022.4
Ahead-Of-Real-Time (ART): A Methodology for Static Reduction of Worst-Case Execution Time

Authors: Daniele Cattaneo, Gabriele Magnani, Stefano Cherubin, and Giovanni Agosta

Published in: OASIcs, Volume 98, Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022)

Abstract
Precision tuning is an approximate computing technique for trading precision with lower execution time, and it has been increasingly important in embedded and high-performance computing applications. In particular, embedded applications benefit from lower precision in order to reduce or remove the dependency on computationally-expensive data types such as floating point. Amongst such applications, an important fraction are mission-critical tasks, such as control systems for vehicles or medical use-cases. In this context, the usefulness of precision tuning is limited by concerns about verificability of real-time and quality-of-service constraints. However, with the introduction of optimisations techniques based on integer linear programming and rigorous WCET (Worst-Case Execution Time) models, these constraints not only can be verified automatically, but it becomes possible to use precision tuning to automatically enforce these constraints even when not previously possible. In this work, we show how to combine precision tuning with WCET analysis to enforce a limit on the execution time by using a constraint-based code optimisation pass with a state-of-the-art precision tuning framework.
Cite as

Daniele Cattaneo, Gabriele Magnani, Stefano Cherubin, and Giovanni Agosta. Ahead-Of-Real-Time (ART): A Methodology for Static Reduction of Worst-Case Execution Time. In Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022). Open Access Series in Informatics (OASIcs), Volume 98, pp. 4:1-4:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

Copy BibTex To Clipboard

@InProceedings{cattaneo_et_al:OASIcs.NG-RES.2022.4,
  author =	{Cattaneo, Daniele and Magnani, Gabriele and Cherubin, Stefano and Agosta, Giovanni},
  title =	{{Ahead-Of-Real-Time (ART): A Methodology for Static Reduction of Worst-Case Execution Time}},
  booktitle =	{Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022)},
  pages =	{4:1--4:10},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-221-1},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{98},
  editor =	{Bertogna, Marko and Terraneo, Federico and Reghenzani, Federico},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NG-RES.2022.4},
  URN =		{urn:nbn:de:0030-drops-161120},
  doi =		{10.4230/OASIcs.NG-RES.2022.4},
  annote =	{Keywords: Approximate Computing, Precision Tuning, Worst-Case Execution Time}
}
  • Refine by Type
  • 10 Document/PDF
  • 2 Document/HTML
  • 1 Volume

  • Refine by Publication Year
  • 1 2026
  • 3 2025
  • 7 2022

  • Refine by Author
  • 5 Reghenzani, Federico
  • 3 Li, Jing
  • 2 Agrawal, Kunal
  • 2 Baruah, Sanjoy
  • 2 Bertogna, Marko
  • Show More...

  • Refine by Series/Journal
  • 2 LIPIcs
  • 6 OASIcs
  • 1 DARTS
  • 1 LITES

  • Refine by Classification
  • 6 Computer systems organization → Real-time systems
  • 2 Computer systems organization → Embedded and cyber-physical systems
  • 1 Computer systems organization
  • 1 Computer systems organization → Multicore architectures
  • 1 Computer systems organization → Real-time operating systems
  • Show More...

  • Refine by Keyword
  • 2 Classification
  • 2 Deep Learning
  • 2 IDK classifiers
  • 2 Sensor data streams
  • 1 Adversarial attacks
  • Show More...

Any Issues?
X

Feedback on the Current Page

CAPTCHA

Thanks for your feedback!

Feedback submitted to Dagstuhl Publishing

Could not send message

Please try again later or send an E-mail
© 2023-2026 Schloss Dagstuhl – LZI GmbH Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact