Exploiting Configuration Dependencies for Rapid Area-efficient Customization of Soft-core Processors

Abstract

The large number of possible configurations in modern soft-core processors make it tedious and time consuming to select the optimal configuration for a given application. In this paper, we propose a framework for rapid area-efficient customization of soft-core processors that exploits the dependencies between the various configuration options to prune the design space. Additionally, the proposed technique relies on rapid and accurate estimation models instead of the time consuming synthesis and execution techniques proposed in the existing work. Experimental results based on hand-coded applications and applications from the popular CHStone benchmark suite show that the proposed framework can rapidly and reliably select the best processor configuration for a given application and save an average of 47.58% area over the processor with all the configuration options enabled while achieving similar performance.