Advancements in AI-Based Compiler Optimization Techniques for Machine Learning Workloads
DOI:
https://doi.org/10.26438/ijcse/v13i3.7077Keywords:
AI-based compilers, reinforcement learning, neural architecture search, machine learning, compiler optimizationAbstract
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.
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