AI-Assisted Design-Space Analysis of High-Performance Arm Processors

This work quantifies the impact of microarchitectural features in modern high-performance Arm CPUs. To combat a parameter space that is too large to traverse naively, we employ a decision tree regression machine learning model to predict the number of execution cycles with 93.38% accuracy compared t...

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Veröffentlicht in:	SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis S. 1455 - 1467
Hauptverfasser:	Moore, Joseph, Deakin, Tom, McIntosh-Smith, Simon
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 17.11.2024
Schlagworte:	aarch64 Adaptation models Codes Computational modeling Data models Machine learning Performance gain Predictive models Random access memory Registers simeng simulation SST vectorization Vectors
Online-Zugang:	Volltext
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