The Dynamic Grey Radial Basis Function Prediction Model and its Applications
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 2
Informed Microarchitecture Design Space Exploration Using Workload Dynamics
Proceedings of the 40th Annual IEEE/ACM International Symposium on Microarchitecture
Characterizing the Effect of Microarchitecture Design Parameters on Workload Dynamic Behavior
IISWC '07 Proceedings of the 2007 IEEE 10th International Symposium on Workload Characterization
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The trend toward multi-/many- core processors will result in sophisticated large-scale architecture substrates that exhibit increasingly complex and heterogeneous behavior. Existing methods lack the ability to accurately and informatively forecast the complex behavior of large and distributed architecture substrates across the design space. Grey neural network is an innovative intelligent computing approach that combines grey system model and neural network. Grey neural network makes full use of the similarities and complementarity between grey system model and neural network to overcome the disadvantage of individual method. In this paper, we propose to use grey neural network to predict 2D space parameters produced by wavelet analysis,which can efficiently reason the characteristics of large and sophisticated multi-core oriented architectures during the design space exploration stage with less samples rather than using detailed cycle-level simulations. Experimental results show that the models achieve high accuracy while maintaining low complexity and computation overhead.