Original Contribution: Stacked generalization
Neural Networks
System-level exploration for pareto-optimal configurations in parameterized systems-on-a-chip
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
TriMedia CPU64 Design Space Exploration
ICCD '99 Proceedings of the 1999 IEEE International Conference on Computer Design
Time-Energy Design Space Exploration for Multi-Layer Memory Architectures
Proceedings of the conference on Design, automation and test in Europe - Volume 1
Cache Optimization For Embedded Processor Cores: An Analytical Approach
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
Efficient design space exploration of high performance embedded out-of-order processors
Proceedings of the conference on Design, automation and test in Europe: Proceedings
Accurate and efficient regression modeling for microarchitectural performance and power prediction
Proceedings of the 12th international conference on Architectural support for programming languages and operating systems
Efficiently exploring architectural design spaces via predictive modeling
Proceedings of the 12th international conference on Architectural support for programming languages and operating systems
A Predictive Performance Model for Superscalar Processors
Proceedings of the 39th Annual IEEE/ACM International Symposium on Microarchitecture
Multi-objective design space exploration of embedded systems
Journal of Embedded Computing - Low-power Embedded Systems
Efficient design space exploration for application specific systems-on-a-chip
Journal of Systems Architecture: the EUROMICRO Journal
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
DATE '12 Proceedings of the Conference on Design, Automation and Test in Europe
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In this paper, we introduce a novel modeling technique to reduce the time associated with cycle-accurate simulation of parallel applications deployed on many-core embedded platforms. We introduce an ensemble model based on artificial neural networks that exploits (in the training phase) multiple levels of simulation abstraction, from cycle-accurate to cycle-approximate, to predict the cycle-accurate results for unknown application configurations. We show that high-level modeling can be used to significantly reduce the number of low-level model evaluations provided that a suitable artificial neural network is used to aggregate the results. We propose a methodology for the design and optimization of such an ensemble model and we assess the proposed approach for an industrial simulation framework based on STMicroelectronics STHORM (P2012) many-core computing fabric.