Fast and Accurate Context-Aware Basic Block Timing Prediction using Transformers

This paper introduces ORXESTRA, a context-aware execution time prediction model based on Transformers XL, specifically designed to accurately estimate performance in embedded system applications. Unlike traditional machine learning models that often overlook contextual information, resulting in biased predictions for individual isolated basic blocks, ORXESTRA overcomes this limitation by incorporating execution context awareness. By doing so, ORXESTRA effectively accounts for the processor micro-architecture without explicitly modeling micro-architectural elements such as caches, pipelines, and branch predictors. Our evaluations demonstrate ORXESTRA's ability to provide precise timing estimations for different ARM targets (Cortex M4, M7, A53, and A72), surpassing existing machine learning-based approaches in both prediction accuracy and prediction speed.