Design Space Exploration based on multiobjective genetic algorithms and clustering-based high-level estimation

A desirable characteristic in high-level synthesis (HLS) is fast search and analysis of implementation alternatives with low or none intervention. This process is known as Design Space Exploration (DSE) and it requires an efficient search method. The employment of intelligent techniques like evolutionary algorithms has been investigated as an alternative to DSE. They turn possible to reduce the search time through selection of higher potential regions of the solution space. We propose here the development of a DSE approach based on a multiobjective evolutionary algorithm (MOEA) and machine learning techniques. It must be employed to indicate the code transformations and architectural parameters adopted in design solution. Furthermore, DSE will use a high-level estimator model to evaluate candidate solutions. Such model must be able to provide a good estimation of energy consumption and execution time at early stages of design.