Advancements in AI-Based Compiler Optimization Techniques for Machine Learning Workloads - Shankar, Vasuki (International Journal of Computer Sciences and Engineering, 2025)
This paper primarily explores the application of AI-driven compiler optimization techniques for machine learning (ML) workloads, with a focus on reinforcement learning and neural architecture search. It examines the performance of traditional compilers compared to AI-optimized compilers leveraging various ML models, including CNNs, RNNs, FNNs, and transformers. The results indicate that AI-driven compilers — particularly those using a hybrid RL + NAS approach—outperforms traditional compilers in energy consumption, memory usage, execution time and hardware utilization. Additionally, the findings suggest that AI-based optimization techniques can streamline ML pipeline development, enhancing efficiency and performance for both resource-constrained environments and large-scale applications.