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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Bibliographic Details

Published in:	2025 62nd ACM/IEEE Design Automation Conference (DAC) pp. 1 - 7
Main Authors:	Pelke, Rebecca, Bosbach, Nils, Reimann, Lennart M., Leupers, Rainer
Format:	Conference Proceeding
Language:	English
Published:	IEEE 22.06.2025
Subjects:	Autotuning cache optimization Design automation Estimation gem5 Hardware Machine learning Optimization Predictive models Scalability TVM
Online Access:	Get full text
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