Storage-Aware Value Prediction

Abstract

Despite the huge potential, value predictors have not been used in modern processors. This is partially due to the complex structures associated with such predictors. In this paper we study value predictors and investigate solutions to reduce storage requirements while imposing negligible coverage cost. Our solutions build on the observation that conventional value predictors do not utilize storage efficiently as they allocate too much space for small and frequently appearing values. We measure data width requirement and entropy in a subset of predictor resources and show that values stored in predictors show limited sizes and very small entropy. We exploit this behavior and suggest different bit sharing solutions for predictors storing single byte values.