Grey Neural Network Based Predictive Model for Multi-core Architecture 2D Spatial Characteristics
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
A Predictive Model for Dynamic Microarchitectural Adaptivity Control
MICRO '43 Proceedings of the 2010 43rd Annual IEEE/ACM International Symposium on Microarchitecture
A first-order mechanistic model for architectural vulnerability factor
Proceedings of the 39th Annual International Symposium on Computer Architecture
Microarchitectural design space exploration made fast
Microprocessors & Microsystems
Dynamic microarchitectural adaptation using machine learning
ACM Transactions on Architecture and Code Optimization (TACO)
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Journal of Systems Architecture: the EUROMICRO Journal
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Program runtime characteristics exhibit significant variation. As microprocessor architectures become more complex, their efficiency depends on the capability of adapting with workload dynamics. Moreover, with the approaching billion-transistor microprocessor era, it is not always economical or feasible to design processors with thermal cooling and reliability redundancy capabilities that target an application's worst case scenario. Therefore, analyzing complex workload dynamics early, at the microarchitecture design stage, is crucial to forecast workload runtime behavior across architecture design alternatives and evaluate the efficiency of workload scenario- based architecture optimizations. Existing methods focus exclusively on predicting aggregated workload behavior. In this paper, we propose accurate and efficient techniques and models to reason about workload dynamics across the microarchitecture design space without using detailed cycle- level simulations. Our proposed techniques employ wavelet- based multiresolution decomposition and neural network based non-linear regression modeling. We extensively evaluate the efficiency of our predictive models in forecasting performance, power and reliability domain workload dynamics that the SPEC CPU 2000 benchmarks manifest on high-performance microprocessors with a microarchitecture design space that consists of 9 key parameters. Our results show that the models achieve high accuracy in revealing workload dynamic behavior across a large microarchitecture design space. We also demonstrate that the proposed techniques can be used to efficiently explore workload scenario-driven architecture optimizations.