PROARTIS: Probabilistically Analysable Real-Time Systems

Static Timing Analysis is the state-of-the-art practice to ascertain the timing behaviour of current-generation real-time embedded systems. The adoption of more complex hardware to respond to the increasing demand for computing power in next-generation systems exacerbates some of the limitations of Static Timing Analysis. In particular, the effort of acquiring (1) detail information on the hardware to develop an accurate model of its execution latency as well as (2) knowledge of the timing behaviour of the program in the presence of varying hardware conditions, such as those dependent on the history of previously executed instructions. We call these problems the Timing Analysis Walls. In this vision-statement paper we present Probabilistic Timing Analysis, a novel approach to the analysis of the timing behaviour of next-generation real-time embedded systems. We show how Probabilistic Timing Analysis attacks the Timing Analysis Walls; we then illustrate the mathematical foundations on which this method is based and the challenges we face in the effort of efficiently implementing it. We also present experimental evidence that shows how Probabilistic Timing Analysis reduces the extent of knowledge about the execution platform required to produce probabilistically-safe and tight WCET estimations

Women and adolescent girls’ experience with COVID-19 in rural Senegal

International audienceSenegal reported its first case of COVID-19 on March 2, 2020. The government responded within two weeks, introducing preventive measures to slow the spread of the virus, including the declaration of a public health emergency, border closures, and the prohibition of intercity travel and gatherings. These measures also slowed economic activity throughout the country and disrupted food supply chains and markets, contributing to loss of livelihoods, income, and households’ purchasing power. Evidence suggests that globally, women have been hit harder by the COVID-19 crisis, in particular with respect to impacts on economic security, health, education, and increased caretaking responsibilities in the household

Measurement-Based Probabilistic Timing Analysis for Multi-path Programs

International audienceThe rigorous application of static timing analysis requires a large and costly amount of detail knowledge on the hardware and software components of the system. Probabilistic Timing Analysis has potential for reducing the weight of that demand. In this paper, we present a sound measurement-based probabilistic timing analysis technique based on Extreme Value Theory. In all the experiments made as part of this work, the timing bounds determined by our technique were less than 15% pessimistic in comparison with the tightest possible bounds obtainable with any probabilistic timing analysis technique. As a point of interest to industrial users, our technique also requires a comparatively low number of measurement runs of the program under analysis; less than 650 runs were needed for the benchmarks presented in this paper

PROARTIS: Probabilistically Analyzable Real-Time System

International audienceStatic timing analysis is the state-of-the-art practice of ascertaining the timing behavior of current-generation real-time embedded systems. The adoption of more complex hardware to respond to the increasing demand for computing power in next-generation systems exacerbates some of the limitations of static timing analysis. In particular, the effort of acquiring (1) detailed information on the hardware to develop an accurate model of its execution latency as well as (2) knowledge of the timing behavior of the program in the presence of varying hardware conditions, such as those dependent on the history of previously executed instructions. We call these problems the timing analysis walls. In this vision-statement article, we present probabilistic timing analysis, a novel approach to the analysis of the timing behavior of next-generation real-time embedded systems. We show how probabilistic timing analysis attacks the timing analysis walls; we then illustrate the mathematical foundations on which this method is based and the challenges we face in the effort of efficiently implementing it. We also present experimental evidence that shows how probabilistic timing analysis reduces the extent of knowledge about the execution platform required to produce probabilistically accurate WCET estimations