Advancements in AI-Based Compiler Optimization Techniques for Machine Learning Workloads
Vasuki Shankar1
- Nvidia Corporation, Bengaluru, Karnataka, India.
Section:Review Paper, Product Type: Journal Paper
Volume-13 ,
Issue-3 , Page no. 70-77, Mar-2025
CrossRef-DOI: https://doi.org/10.26438/ijcse/v13i3.7077
Online published on Mar 31, 2025
Copyright © Vasuki Shankar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Citation
IEEE Style Citation: Vasuki Shankar, “Advancements in AI-Based Compiler Optimization Techniques for Machine Learning Workloads,” International Journal of Computer Sciences and Engineering, Vol.13, Issue.3, pp.70-77, 2025.
MLA Citation
MLA Style Citation: Vasuki Shankar "Advancements in AI-Based Compiler Optimization Techniques for Machine Learning Workloads." International Journal of Computer Sciences and Engineering 13.3 (2025): 70-77.
APA Citation
APA Style Citation: Vasuki Shankar, (2025). Advancements in AI-Based Compiler Optimization Techniques for Machine Learning Workloads. International Journal of Computer Sciences and Engineering, 13(3), 70-77.
BibTex Citation
BibTex Style Citation:
@article{Shankar_2025,
author = {Vasuki Shankar},
title = {Advancements in AI-Based Compiler Optimization Techniques for Machine Learning Workloads},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {3 2025},
volume = {13},
Issue = {3},
month = {3},
year = {2025},
issn = {2347-2693},
pages = {70-77},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5785},
doi = {https://doi.org/10.26438/ijcse/v13i3.7077}
publisher = {IJCSE, Indore, INDIA},
}
RIS Citation
RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v13i3.7077}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5785
TI - Advancements in AI-Based Compiler Optimization Techniques for Machine Learning Workloads
T2 - International Journal of Computer Sciences and Engineering
AU - Vasuki Shankar
PY - 2025
DA - 2025/03/31
PB - IJCSE, Indore, INDIA
SP - 70-77
IS - 3
VL - 13
SN - 2347-2693
ER -
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Abstract
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.
Key-Words / Index Term
AI-based compilers, reinforcement learning, neural architecture search, machine learning, compiler optimization.
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