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Regression Ensembles for Fast Design Space Exploration of Heterogeneous Hardware Designs

In architectural design of embedded systems, machine learning (ML) has become a promising solution to provide robustness to the design space exploration (DSE) of large hardware designs. However, given the large diversity of embedded applications, a main challenge in the design of a high-accuracy predictor is to select one ML algorithm to encompass a wide range of applications. In this context, regression ensemble is a promising solution since it can use multiple models and combine their predictions. In this work we employ the use of ensemble methods to predict performance when running different applications in different heterogeneous designs composed of a general purpose processor (GPPs) and a reconfigurable accelerator (RA). In our investigation, we evaluate three ensemble methods, Random Forest, AdaBoosting and Gradient Boosting. So, we compare them to the most used regression algorithms found in literature to perform DSE of computer architectures. Results show an error prediction rate below 2% on average when using ensemble methods and a throughput of more than 5,000 predictions per second when using Gradient Boosting.