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

Abstract

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 Xtensa customizable processor architecture from Tensilica and a very long instruction word (VLIW) processor architecture are considered as example target platforms to impart the domain knowledge for the proposed model. Case studies in support of the proposed model are presented in order to understand how BBN can be used for design space pruning by propagating the evidence and arriving at probabilistic inferences to ease the decision-making process. The results show that the design space reduces drastically from a few million design options available to just less than one hundred for Xtensa architecture and from a few billions of design options available to just few thousands for VLIW architecture. The work also validates the pruned design points for their optimality.