Evaluating Memory Architectures for Media Applications on Coarse-Grained Reconfigurable Architectures

Abstract

Reconfigurable ALU Array (RAA) architectures - representing a popular class of Coarse-grained Reconfigurable Architectures - are gaining in popularity especially for media applications due to their flexibility, regularity, and efficiency. In such architectures, memory is critical not only for configuration data but also for the heavy data traffic required by the application. In this paper, we offer a scheme for system designers to quickly estimate the performance of media applications on RAA architectures. Our experimental results demonstrate the flexibility of our memory architecture evaluation scheme as well as the varying effects of the memory architectures on the application performance.