Efficient system design space exploration using machine learning techniques
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The vast number of transistors available through modern fabrication technology gives architects an unprecedented amount of freedom in chip-multiprocessor (CMP) designs. However, such freedom translates into a design space that is impossible to fully, or even partially to any significant frac- tion, explore through detailed simulation. In this paper we propose to address this problem using predictive modeling, a well-known machine learning technique. More specifi- cally we build models that, given only a minute fraction of the design space, are able to accurately predict the behav- ior of the remaining designs orders of magnitude faster than simulating them. In contrast to previous work, our models can predict per- formance metrics not only for unseen CMP configurations for a given application, but also for unseen configurations of a new application that was not in the set of applications used to build the model, given only a very small number of results for this new application. We perform extensive experiments to show the efficacy of the technique for exploring the design space of CMP's running parallel applications. The technique is used to pre- dict both energy-delay and execution time. Choosing both explicitly parallel applications and applications that are parallelized using the thread-level speculation (TLS) ap- proach, we evaluate performance on a CMP design space with about 95 million points using 18 benchmarks with up to 1000 training points each. For predicting the energy- delay metric, prediction errors for unseen configurations of the same application range from 2.4% to 4.6% and for con- figurations of new applications from 3.1% to 4.9%.