Description

The course aims at providing the basics for designing optimal embedded applications starting from a given problem. The course, configured to stimulate the interaction with the students, will address the following methodological aspects

• Problem complexity and complexity reduction (deterministic vs probabilistic approaches for problem solving; Randomized algorithms)

• Approximate computing (sources of approximation, Probably approximately correct computation)

• Optimization methods for embedded applications (gradient-based optimization, evolutionary-based optimization, learning mechanisms)

• Application porting to low precision hardware platforms (robustness analysis in the small; robustness analysis in the large; accuracy loss estimation)

• Performance and quality assessment of the solution (Crossvalidation, bootstrap, bags of little bootstraps)

References

• C.Alippi, Intelligence for Embedded Systems: a Methodological approach, Springer, 2014

• Technical papers and reference material provided by the professor