Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Machine Learning
Active learning via transductive experimental design
ICML '06 Proceedings of the 23rd international conference on Machine learning
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Efficient system design space exploration using machine learning techniques
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Decision-theoretic design space exploration of multiprocessor platforms
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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IEEE Transactions on Evolutionary Computation
"Smart" design space sampling to predict Pareto-optimal solutions
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This paper makes several contributions to address the challenge of supervising HLS tools for design space exploration (DSE). We present a study on the application of learning-based methods for the DSE problem, and propose a learning model for HLS that is superior to the best models described in the literature. In order to speedup the convergence of the DSE process, we leverage transductive experimental design, a technique that we introduce for the first time to the CAD community. Finally, we consider a practical variant of the DSE problem, and present a solution based on randomized selection with strong theory guarantee.