Large Language Models for EDA Cloud Job Resource and Lifetime Prediction

Yuxuan Yin, Boxun Xu, Peng Li

Abstract:

The rapid growth of cloud computing in the Electronic Design Automation (EDA) industry has created a
critical need for resource and job lifetime prediction to achieve optimal scheduling. Traditional machine
learning methods often struggle with the complexity and heterogeneity of EDA workloads, requiring
extensive feature engineering and domain expertise. We propose a novel framework that fine-tunes
Large Language Models (LLMs) to address this challenge through text-to-text regression. We introduce
the scientific notation and prefix filling to constrain the LLM, significantly improving output format
reliability. Moreover, we found that full-attention finetuning and inference improves the prediction
accuracy of sliding-window-attention LLMs. We demonstrate the effectiveness of our proposed framework
on real-world cloud datasets, setting a new baseline for performance prediction in the EDA domain.