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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Vydané v:	2025 62nd ACM/IEEE Design Automation Conference (DAC) s. 1 - 7
Hlavní autori:	Pelke, Rebecca, Bosbach, Nils, Reimann, Lennart M., Leupers, Rainer
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 22.06.2025
Predmet:	Autotuning cache optimization Design automation Estimation gem5 Hardware Machine learning Optimization Predictive models Scalability TVM
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