Instruction cache optimizations for embedded systems

Ph.DDOCTOR OF PHILOSOPH

Structural analysis of combinatorial optimization problem characteristics and their resolution using hybrid approaches

Many combinatorial problems coming from the real world may not have a clear and well defined structure, typically being dirtied by side constraints, or being composed of two or more sub-problems, usually not disjoint. Such problems are not suitable to be solved with pure approaches based on a single programming paradigm, because a paradigm that can effectively face a problem characteristic may behave inefficiently when facing other characteristics. In these cases, modelling the problem using different programming techniques, trying to ”take the best” from each technique, can produce solvers that largely dominate pure approaches. We demonstrate the effectiveness of hybridization and we discuss about different hybridization techniques by analyzing two classes of problems with particular structures, exploiting Constraint Programming and Integer Linear Programming solving tools and Algorithm Portfolios and Logic Based Benders Decomposition as integration and hybridization frameworks

Hardware/Software Co-Synthesis with Memory Hierarchies

This paper introduces the first hardware/software cosynthesis algorithm of distributed real-time systems that optimizes memory hierarchy along with the rest of the architecture. Our algorithm synthesize a set of real-time tasks with data dependencies onto a heterogeneous multiprocessor architecture that meets the performance constraints with minimized cost. Our algorithm chooses cache sizes and allocates tasks to caches as part of co-synthesis. Experimental results, including examples from the literature and results on an MPEG-2 encoder, show that our algorithm is efficient and compared with existing algorithms, it can reduce the overall cost of the synthesized system.