The Case for Learning Application Behavior to Improve Hardware Energy Efficiency

TL;DR

This paper introduces FORECASTER, a deep learning-based approach that learns application behavior to dynamically reconfigure hardware, significantly improving energy efficiency with minimal performance loss.

Contribution

It presents a novel deep learning model for hardware reconfiguration based on application behavior, achieving substantial power savings over prior methods.

Findings

FORECASTER saves up to 18.4% system power.

Average power savings of 16% with negligible performance impact.

Outperforms previous reconfiguration approaches by 7% in power savings.

Abstract

Computer applications are continuously evolving. However, significant knowledge can be harvested from a set of applications and applied in the context of unknown applications. In this paper, we propose to use the harvested knowledge to tune hardware configurations. The goal of such tuning is to maximize hardware efficiency (i.e., maximize an applications performance while minimizing the energy consumption). Our proposed approach, called FORECASTER, uses a deep learning model to learn what configuration of hardware resources provides the optimal energy efficiency for a certain behavior of an application. During the execution of an unseen application, the model uses the learned knowledge to reconfigure hardware resources in order to maximize energy efficiency. We have provided a detailed design and implementation of FORECASTER and compared its performance against a prior state-of-the-art hardware reconfiguration approach. Our results show that FORECASTER can save as much as 18.4% system power over the baseline set up with all resources. On average, FORECASTER saves 16% system power over the baseline setup while sacrificing less than 0.01% of overall performance. Compared to the prior scheme, FORECASTER increases power savings by 7%.