A BBN-Based Framework for Design Space Pruning of Application Specific Instruction Processors

During the synthesis phase of the embedded system design process, the designer has to take early decisions for selecting the optimal system components such as processors, memories, communication interfaces, etc. from the available huge design alternatives. In order to obtain the optimal design configurations from the available huge design alternatives, an efficient design space pruning technique that will ease the design space exploration (DSE) process is required. The knowledge about the target architectural parameters affecting the overall objectives of the system should be considered during the design, so that the search process for finding the optimal system configurations will be rapid and more efficient. The Bayesian belief network (BBN)-based modeling framework for design space pruning proposed in this paper attempts to resolve the existing limitation in imparting domain knowledge and provides a pioneering effort to support the designer during the process of application specific system design. The ...