Partitioning and Data Mapping in Reconfigurable Cache and Scratchpad Memory--Based Architectures

Abstract

Scratchpad memory (SPM) is considered a useful component in the memory hierarchy, solely or along with caches, for meeting the power and energy constraints as performance ceases to be the sole criteria for processor design. Although the efficiency of SPM is well known, its use has been restricted owing to difficulties in programmability. Real applications usually have regions that are amenable to exploitation by either SPM or cache and hence can benefit if the two are used in conjunction. Dynamically adjusting the local memory resources to suit application demand can significantly improve the efficiency of the overall system. In this article, we propose a compiler technique to map application data objects to the SPM-cache and also partition the local memory between the SPM and cache depending on the dynamic requirement of the application. First, we introduce a novel graph-based structure to tackle data allocation in an application. Second, we use this to present a data allocation heuristic to map program objects for a fixed-size SPM-cache hybrid system that targets whole program optimization. We finally extend this formulation to adapt the SPM and cache sizes, as well as the data allocation as per the requirement of different application regions. We study the applicability of the technique on various workloads targeted at both SPM-only and hardware reconfigurable memory systems, observing an average of 18% energy-delay improvement over state-of-the-art techniques.