Introducing Instruction-Accurate Simulators for Performance Estimation of Autotuning Workloads

Accelerating Machine Learning (ML) workloads requires efficient methods due to their large optimization space. Autotuning has emerged as an effective approach for systematically evaluating variations of implementations. Traditionally, autotuning requires the workloads to be executed on the target ha...

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Veröffentlicht in:	2025 62nd ACM/IEEE Design Automation Conference (DAC) S. 1 - 7
Hauptverfasser:	Pelke, Rebecca, Bosbach, Nils, Reimann, Lennart M., Leupers, Rainer
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 22.06.2025
Schlagworte:	Autotuning cache optimization Design automation Estimation gem5 Hardware Machine learning Optimization Predictive models Scalability TVM
Online-Zugang:	Volltext
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