Machine Learning for Energy-Performance-aware Scheduling

TL;DR

This paper introduces a Bayesian Optimization approach using Gaussian Processes to efficiently find optimal energy-performance trade-offs in embedded system scheduling, providing interpretability and addressing multi-objective challenges.

Contribution

It presents a novel Bayesian Optimization framework with sensitivity analysis for multi-objective scheduling on heterogeneous architectures, enhancing interpretability and efficiency.

Findings

Effective Pareto frontier approximation between energy and time.

Insight into hardware parameters influencing performance.

Comparison of covariance kernels for model interpretability.

Abstract

In the post-Dennard era, optimizing embedded systems requires navigating complex trade-offs between energy efficiency and latency. Traditional heuristic tuning is often inefficient in such high-dimensional, non-smooth landscapes. In this work, we propose a Bayesian Optimization framework using Gaussian Processes to automate the search for optimal scheduling configurations on heterogeneous multi-core architectures. We explicitly address the multi-objective nature of the problem by approximating the Pareto Frontier between energy and time. Furthermore, by incorporating Sensitivity Analysis (fANOVA) and comparing different covariance kernels (e.g., Mat\'ern vs. RBF), we provide physical interpretability to the black-box model, revealing the dominant hardware parameters driving system performance.