High-level power and performance estimation of FPGA-based soft processors and its application to design space exploration

This paper presents a design space exploration framework for an FPGA-based soft processor that is built on the estimation of power and performance metrics using algorithm and architecture parameters. The proposed framework is based on regression trees, a popular machine learning technique, that can capture the relationship of low-level soft-processor parameters and high-level algorithm parameters of a specific application domain, such as image compression. In doing this, power and execution time of an algorithm can be predicted before implementation and on unseen configurations of soft processors. For system designers this can result in fast design space exploration at an early stage in design.