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    Middleware for Adaptive Reflective computer Systems


    MARS consists of a toolchain for creating resource managers that allows users to easily compose models and policies that interact in a hierarchy defined by the granularity of the actuations performed in the system. MARS is implemented and evaluated on top of a real Linux-based platform. Furthermore, MARS also provides an offline simulation infrastructure for fast prototyping of policies and large-scale or long-term policy evaluation.

    My contributions to MARS include extensions to support a custom offline simulator, as well as the gem5 architectural simulator. I have used MARS to deploy and evaluate resource management policies for both simulated multicores and real Linux platforms such as the ODROID XU3.

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    Information Processing Factory

    IPF aims to show how self-awareness can be achieved across multiple abstraction levels, and discuss new research challenges.


    The project exploits self-awareness principles, together with lessons learned from large-scale factories to contain complexity, achieve predictability and manage robust system design. The overall research theme will demonstrate the utility of self-aware IPFs in managing MPSoC complexity, while achieving scalability, predictability, and system efficiency, with the long term goal of supporting autonomous systems as a main application.

    My work in this area focuses on providing adaptive resource management using hierarchical control and goal-driven autonomy.


Sandra Hernandez Herrero - KTH/UMadrid - MSc Student - "Design Space Exploration for Visual-Inertial SLAM," with Jose Araujo, 2020/2021