Efficient Cache Reconfiguration Using Machine Learning in NoC-Based Many-Core CMPs

Abstract

Dynamic cache reconfiguration (DCR) is an effective technique to optimize energy consumption in many-core architectures. While early work on DCR has shown promising energy saving opportunities, prior techniques are not suitable for many-core architectures since they do not consider the interactions and tight coupling between memory, caches, and network-on-chip (NoC) traffic. In this article, we propose an efficient cache reconfiguration framework in NoC-based many-core architectures. The proposed work makes three major contributions. First, we model a distributed directory based many-core architecture similar to Intel Xeon Phi architecture. Next, we propose an efficient cache reconfiguration framework that considers all significant components, including NoC, caches, and main memory. Finally, we propose a machine learning--based framework that can reduce the exploration time by an order of magnitude with negligible loss in accuracy. Our experimental results demonstrate 18.5% energy savings on average compared to base cache configuration.