🤖 AI Summary
In the post-Dennard era, embedded systems face intricate trade-offs between energy efficiency and latency, rendering traditional heuristic methods ineffective in navigating the high-dimensional, non-smooth scheduling space. This work proposes a Gaussian process-based multi-objective Bayesian optimization framework to automatically discover Pareto-optimal scheduling strategies that balance energy consumption and execution time on heterogeneous multicore architectures. By integrating fANOVA sensitivity analysis and comparing multiple covariance kernels—such as Matérn and RBF—the approach endows the black-box optimizer with physical interpretability, uncovering how key hardware parameters influence system performance. Experimental results demonstrate that the method efficiently approximates the Pareto front, significantly advancing both the automation of scheduling and the understanding of underlying hardware behaviors.
📝 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.