Accurate and efficient regression modeling for microarchitectural performance and power prediction

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Informed Microarchitecture Design Space Exploration Using Workload Dynamics (2007, unknown): “Lee and Brooks [9, 11] proposed regression on cubic splines for predicting the performance and power of applications executing on microprocessor configurations in a large microarchitectural design space.”
Efficiently exploring architectural design spaces via predictive modeling (2006, background): “Lee and Brooks [21] propose regression on cubic splines (piecewise polynomials) for predicting performance and power for applications executing on microprocessor configurations in a large microarchitectural design space.”
Real time power estimation and thread scheduling via performance counters (2009, methodology): “[12] use statistical inference models to predict power consumption based on hardware design parameters.”
Estimation of energy consumption in machine learning (2019, methodology): “[76] Instruction Current Analytical Energy Profiling CPU Model Yes [10] Hardware Analytical Analytical Power, Energy, Perf Simu data CPU Theoretical Yes [80] Hardware Analytical Transition-sensitive Energy/Power Simulation CPU, f units Theoretical – [44] Hardware Real Heuristics Power PMC, sim data ”