#MLO Readme
The optimization of reconfigurable applications often requires substantial effort from the designer who has to analyze the application, create models and benchmarks and subsequently use them to optimize the application. One could try to employ exhaustive search of the application parameter space to carry out optimization yet it is unrealistic since benchmark evaluations involve bit-stream generation and code execution which takes hours of computing time. Recently it has been shown useful to use surrogate models combined with fitness functions for computationally expensive optimization problems in various fields. As these models are orders of magnitude cheaper, they can substantially decrease optimization cost thus allowing for an automated approach. This is the motivation behind Machine Learning Optimizer which we apply to non-linear and multi-modal problem of heterogeneous application parameter optimization. We use regressors to model performance of the design like execution time or throughput, while searching for the global optimum using meta-heuristics. We classify the parameter space using support vector machines to identify designs that would fail constraints; over-map on resources, produce inaccurate results or other.
##Directory Structure
Contains all of the reports, papers and documentation related to MLO development.
For each paper published and related to MLO please create a seperate folder containing the relevant LaTeX code. The naming convention for the folder is as follows: conferencename_submissionnumber
Contains reconfigurable computing fitness function examples. The fitness functions in this directory should be based on well documentated papers, and used for research purpose. Preferably csv files containing the fitness functions and scripts used to obtain this are provided.
For detailed architecture of the application please refer to doc/groupproj-rep13.pdf
##Version convention vA.B.C.
Within a revision starting with number A the fitness and configuration scripts should be compatible.
Minor revisions can introduce changes to the methodology of upkeeping of application state. Before updating a minor revison please ensure that all of the current runs and trials have finished.
Extra features added with no implication on the state of the application.