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Documents authored by Serra, Isabel

Document
DOI: 10.4230/LIPIcs.ECRTS.2022.4
Using Quantile Regression in Neural Networks for Contention Prediction in Multicore Processors

Authors: Axel Brando, Isabel Serra, Enrico Mezzetti, Jaume Abella, and Francisco J. Cazorla

Published in: LIPIcs, Volume 231, 34th Euromicro Conference on Real-Time Systems (ECRTS 2022)

Abstract
The development of multicore-based embedded real-time systems is a complex process that encompasses several phases. During the software design and development phases (DDP), and prior to the validation phase, key decisions are taken that cover several aspects of the system under development, from hardware selection and configuration, to the identification and mapping of software functions to the processing nodes. The timing dimension steers a large fraction of those decisions as the correctness of the final system ultimately depends on the implemented functions being able to execute within the allotted time budgets. Early execution time figures already in the DDP are thus needed to prevent flawed design decisions resulting in timing misbehavior being intercepted at the timing analysis step in the advanced development phases, when rolling back to different design decisions is extremely onerous. Multicore timing interference compounds this situation as it has been shown to largely impact execution time of tasks and, therefore, must be factored in when deriving early timing bounds. To effectively prevent misconfigurations while preserving resource efficiency, early timing estimates, typically derived from previous projects or early versions of the software functions, should conservatively and tightly overestimate the timing requirements of the final system configuration including multicore contention. In this work, we show that multi-linear regression (MLR) models and neural network (NN) models can be used to predict the impact of multicore contention on tasks' execution time and hence, derive contention-aware early time budgets, as soon as a release (binary) of the application is available. However, those techniques widely used in the mainstream domain minimize the average/mean case and the predicted impact of contention frequently underestimates the impact that can potentially arise at run time. In order to cover this gap, we propose the use of quantile regression neural networks (QRNN), which are specifically designed to predict the desired high quantile. QRNN reduces the number of underestimations compared to MLR and NN models while containing the overestimation by preserving the high quality prediction. For a set of workloads composed by representative kernels running on a NXP T2080 processor, QRNN reduces the number of underestimations to 8.8% compared to 46.8% and 31.3% for MLR and NN models respectively, while keeping the average over estimation in 1%. QRNN exposes a parameter, the target quantile, that allows controlling the behavior of the predictions so it adapts to user’s needs.
Cite as

Axel Brando, Isabel Serra, Enrico Mezzetti, Jaume Abella, and Francisco J. Cazorla. Using Quantile Regression in Neural Networks for Contention Prediction in Multicore Processors. In 34th Euromicro Conference on Real-Time Systems (ECRTS 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 231, pp. 4:1-4:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{brando_et_al:LIPIcs.ECRTS.2022.4,
  author =	{Brando, Axel and Serra, Isabel and Mezzetti, Enrico and Abella, Jaume and Cazorla, Francisco J.},
  title =	{{Using Quantile Regression in Neural Networks for Contention Prediction in Multicore Processors}},
  booktitle =	{34th Euromicro Conference on Real-Time Systems (ECRTS 2022)},
  pages =	{4:1--4:25},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-239-6},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{231},
  editor =	{Maggio, Martina},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2022.4},
  URN =		{urn:nbn:de:0030-drops-163213},
  doi =		{10.4230/LIPIcs.ECRTS.2022.4},
  annote =	{Keywords: Neural Networks, Quantile Prediction, Multicore Contention}
}
Document
DOI: 10.4230/LIPIcs.ECRTS.2022.20
Using Markov’s Inequality with Power-Of-k Function for Probabilistic WCET Estimation

Authors: Sergi Vilardell, Isabel Serra, Enrico Mezzetti, Jaume Abella, Francisco J. Cazorla, and Joan del Castillo

Published in: LIPIcs, Volume 231, 34th Euromicro Conference on Real-Time Systems (ECRTS 2022)

Abstract
Deriving WCET estimates for software programs with probabilistic means (a.k.a. pWCET estimation) has received significant attention during last years as a way to deal with the increased complexity of the processors used in real-time systems. Many works build on Extreme Value Theory (EVT) that is fed with a sample of the collected data (execution times). In its application, EVT carries two sources of uncertainty: the first one that is intrinsic to the EVT model and relates to determining the subset of the sample that belongs to the (upper) tail, and hence, is actually used by EVT for prediction; and the second one that is induced by the sampling process and hence is inherent to all sample-based methods. In this work, we show that Markov’s inequality can be used to obtain provable trustworthy probabilistic bounds to the tail of a distribution without incurring any model-intrinsic uncertainty. Yet, it produces pessimistic estimates that we shave substantially by proposing the use of a power-of-k function instead of the default identity function used by Markov’s inequality. Lastly, we propose a method to deal with sampling uncertainty for Markov’s inequality that consistently improves EVT estimates on synthetic and real data obtained from a railway application.
Cite as

Sergi Vilardell, Isabel Serra, Enrico Mezzetti, Jaume Abella, Francisco J. Cazorla, and Joan del Castillo. Using Markov’s Inequality with Power-Of-k Function for Probabilistic WCET Estimation. In 34th Euromicro Conference on Real-Time Systems (ECRTS 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 231, pp. 20:1-20:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{vilardell_et_al:LIPIcs.ECRTS.2022.20,
  author =	{Vilardell, Sergi and Serra, Isabel and Mezzetti, Enrico and Abella, Jaume and Cazorla, Francisco J. and del Castillo, Joan},
  title =	{{Using Markov’s Inequality with Power-Of-k Function for Probabilistic WCET Estimation}},
  booktitle =	{34th Euromicro Conference on Real-Time Systems (ECRTS 2022)},
  pages =	{20:1--20:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-239-6},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{231},
  editor =	{Maggio, Martina},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2022.20},
  URN =		{urn:nbn:de:0030-drops-163377},
  doi =		{10.4230/LIPIcs.ECRTS.2022.20},
  annote =	{Keywords: Markov’s inequality, probabilistic time estimates, probabilistic WCET, Extreme Value Theory}
}
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