Abstract
Dynamic cache reconfiguration (DCR) is an effective technique to optimize energy consumption inmany-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.
Original language | English (US) |
---|---|
Article number | 60 |
Journal | ACM Transactions on Design Automation of Electronic Systems |
Volume | 24 |
Issue number | 6 |
DOIs | |
State | Published - Nov 2019 |
Keywords
- Cache reconfiguration
- Machine learning
ASJC Scopus subject areas
- Computer Science Applications
- Computer Graphics and Computer-Aided Design
- Electrical and Electronic Engineering