Machine Learning-Based Approaches for Energy-Efficiency Prediction and Scheduling in Composite Cores Architectures

Abstract

Heterogeneous architectures offer divers computing capabilities. Composite Cores Architecture (CCA) is a class of dynamic heterogeneous architectures that empowers the system to build the most appropriate core at run-time for each application by composing cores together to make larger core or decomposing a large core into multiple smaller cores. While CCA provides more flexibility for the running application to find the best run-time configurations to maximize energy-efficiency, due to the interdependence of various tuning parameters such as the core type, run-time voltage and frequency setting, and number of threads, it makes the scheduling more challenging. In this work, we investigate the scheduling challenges of multithreaded applications on CCA architectures. This paper describes a systematic approach to predict the right configurations for running multithreaded workloads on the composite cores architecture. It achieves this by developing a machine learning-based approach to predict core type, voltage and frequency to maximize the energy-efficiency. Our predictor learns offline from an extensive set of training multithreaded workloads. It is then applied to predict the optimal processor configuration at run-time by considering of the multithreaded application's characteristics and the optimization objective. For this purpose, five well-known machine learning models are implemented for energy-efficiency optimization and precisely compared in terms of accuracy and hardware overhead to guide the scheduling decisions in a CCA. The results show that while complex machine learning models such as MultiLayerPerceptron are achieving higher accuracy, after evaluating their implementation overheads, they perform worst in terms of power, accuracy/area and latency as compared to simpler but slightly less accurate regression-based and tree-based classifiers.