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Informed Microarchitecture Design Space Exploration Using Workload Dynamics
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Efficient Design Space Exploration of High Performance Embedded Out-of-Order Processors
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Construction and Use of Linear Regression Models for Processor Performance Analysis
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Illustrative Design Space Studies with Microarchitectural Regression Models
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Constructing a Non-Linear Model with Neural Networks for Workload Characterization
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Towards Efficient Supercomputing: A Quest for the Right Metric
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Spectral analysis for characterizing program power and performance
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Using Wavelet Domain Workload Execution Characteristics to Improve Accuracy, Scalability and Robustness in Program Phase Analysis
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A systematic methodology to compute the architectural vulnerability factors for a high-performance microprocessor
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Runtime power monitoring in high-end processors: methodology and empirical data
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A Predictive Performance Model for Superscalar Processors
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A First-Order Superscalar Processor Model
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Control Flow Modeling in Statistical Simulation for Accurate and Efficient Processor Design Studies
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Wavelet-based phase classification
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Complexity-based program phase analysis and classification
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Performance prediction based on inherent program similarity
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Methods of inference and learning for performance modeling of parallel applications
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Theoretical modeling of superscalar processor performance
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Run-time modeling and estimation of operating system power consumption
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Neural networks for pattern recognition
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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