An efficient framework for dynamic reconfiguration of instruction-set customization

Abstract

We present an efficient framework for dynamic reconfiguration of application-specific instruction-set customization. A key component of this framework is an iterative algorithm for temporal and spatial partitioning of the loop kernels. Our algorithm maximizes performance gain of an application while taking into consideration the dynamic reconfiguration cost. It selects the appropriate custom instruction-sets for the loops and maps them into appropriate configurations. We model the temporal partitioning problem as a k-way graph partitioning problem. A dynamic programming based solution is used for the spatial partitioning. Comprehensive experimental results indicate that our iterative algorithm is highly scalable while producing optimal or near-optimal (99% of the optimal) performance gain. Copyright 2007 ACM.