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Gem5, an open-source, flexible, and cost-effective simulator, is widelyrecognized and utilized in both academic and industry fields for hardwaresimulation. However, the typically time-consuming nature of simulating programson Gem5 underscores the need for a predictive model that can estimatesimulation time. As of now, no such dataset or model exists. In response tothis gap, this paper makes a novel contribution by introducing a unique datasetspecifically created for this purpose. We also conducted analysis of theeffects of different instruction types on the simulation time in Gem5. Afterthis, we employ three distinct models leveraging CodeBERT to execute theprediction task based on the developed dataset. Our superior regression modelachieves a Mean Absolute Error (MAE) of 0.546, while our top-performingclassification model records an Accuracy of 0.696. Our models establish afoundation for future investigations on this topic, serving as benchmarksagainst which subsequent models can be compared. We hope that our contributioncan simulate further research in this field. The dataset we used is availableat https://github.com/XueyangLiOSU/Gem5Pred.

Gem5Pred: Predictive Approaches For Gem5 Simulation Time