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Related Experiment Video

Updated: Sep 9, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Graph neural networks with configuration cross-attention for tensor compilers.

Dmitrii Khizbullin¹, Eduardo Rocha de Andrade², Thanh Hau Nguyen²

¹King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.

Frontiers in Artificial Intelligence

|September 5, 2025

Summary

TGraph accelerates neural network inference by intelligently screening tensor configurations. This AI tensor compiler significantly improves performance and reduces energy consumption in AI data centers.

Keywords:

attention mechanism graph neural network (GNN)machine learning for systems ranking loss function tensor compilation

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Area of Science:

Artificial Intelligence
Computer Science
Machine Learning

Background:

Neural network inference requires efficient computation of tensor operations.
Optimizing tensor layouts (transpositions, tilings) is combinatorially complex.
Existing compilers often rely on heuristics, limiting performance gains.

Purpose of the Study:

To introduce TGraph, a novel neural graph architecture for optimizing neural network inference.
To develop an AI tensor compiler capable of screening for fast tensor configurations.
To reduce the computational cost and energy footprint of AI inference.

Main Methods:

Representing neural network inference as a computational graph.
Utilizing a graph architecture to explore tensor layout permutations.
Developing an AI tensor compiler framework distinct from traditional approaches.

Main Results:

TGraph significantly improves mean Kendall's τ across layout collections, reaching 67.4% compared to a baseline of 29.8%.
The proposed method acts as an AI tensor compiler, outperforming heuristic-based methods.
Estimated potential CO2 emission reduction equivalent to over 50% of household emissions in AI data center regions.

Conclusions:

TGraph offers a powerful new approach to optimizing neural network inference.
The AI tensor compiler framework demonstrates substantial performance improvements.
This work has significant implications for energy efficiency in artificial intelligence.