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Section: Partnerships and Cooperations

International Initiatives

Inria International Labs

Inria@SiliconValley

Associate Team involved in the International Lab:

RIPPES
  • Title: RIgorous Programming of Predictable Embedded Systems

  • International Partner (Institution – Laboratory – Researcher):

    • University of California Berkeley (United States) – Electrical Engineering and Computer Science Department (EECS) – Edward Lee

    • University of Auckland (New Zealand) – Electrical Computer Engineering Department (ECE) – Partha Roop

  • Start year: 2013

  • See also: https://wiki.inria.fr/rippes

  • The Rippes associated teams gathers the Spades team from Inria Grenoble Rhône-Alpes, the Ptolemy group from UC Berkeley (EECS Department), and the Embedded Systems Research group from U. of Auckland (ECE Department). The planned research seeks to reconcile two contradictory objectives of embedded systems, more predictability and more adaptivity. We have addressed these issues by exploring two complementary research directions: (1) by starting from a classical concurrent C or Java programming language and enhancing it to provide more predictability (see Section  7.2.1 ), and (2) by starting from a very predictable model of computation (SDF) and enhancing it to provide more adaptivity (see Section  7.2.3 ).

Inria Associate Teams not involved in an Inria International Labs

Causalysis
  • Title: Causality Analysis for Safety-Critical Embedded Systems

  • International Partner (Institution – Laboratory – Researcher):

    • University of Pennsylvania (United States) – PRECISE center – Oleg Sokolsky

  • Start year: 2015

  • See also: https://team.inria.fr/causalysis

  • Today's embedded systems become more and more complex, while an increasing number of safety-critical functions rely on them. Determining the cause(s) of a system-level failure and elucidating the exact scenario that led to the failure is today a complex and tedious task that requires significant expertise. The CAUSALYSIS project will develop automated approaches to causality analysis on execution logs.