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Methods for predictAble Design of heterogeNeous Embedded System with adaptivity and reliability Support (MADNESS)
Start date: Jan 1, 2010, End date: Mar 31, 2013 PROJECT  FINISHED 

The MADNESS project aims at the definition of innovative system-level design methodologies for embedded systems, able to drive the optimal composition of an heterogeneous MPSoC architecture, according to the requirements and the features of a given target application field.The proposed methodologies will extend the classic concept of design space exploration, to cope with high heterogeneity, technology scaling, system reliability and multi-application demands, pursuing the following objectives:-Improve design predictability of highly heterogeneous embedded systems, bridging the so called "implementation gap", i.e. the gap between the results that can be predicted during the system-level design phase and those eventually obtained after the on-silicon implementation.-Consider, apart from more traditional constraints (typically, cost, performance, power consumption), continued availability of service, taking into account fault recovery as one of the optimization factors to be satisfied.-Support adaptive runtime management of the architecture.The technical approach of the project will rely on the following methods:-In order to improve the design predictability, traditional system-level design methodologies will be extended to consider variables strictly related with physical implementation of the architecture, leveraging a specific layer for rapid and accurate on-hardware FPGA-based emulation.-In order to address fault tolerance and adaptive runtime resources management, new methodologies are going to be defined and included in system-level exploration. Specific hardware/middleware IP modules will be developed to pursue those objectives.The results of the project are expected to have a deep impact on the design flow for high-complexity embedded systems, facilitating and enhancing the exploration of the architectural design space, therefore resulting in a significantly increased overall productivity.
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