Graph neural networks with configuration cross-attention for tensor compilers

Graph neural networks with configuration cross-attention for tensor compilers

With the recent popularity of neural networks comes the need for efficient serving of inference workloads. A neural network inference workload can be represented as a computational graph with nodes as operators transforming multidimensional tensors. The tensors can be transposed and/or tiled in a co...

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Bibliographic Details
Main Authors: Dmitrii Khizbullin, Eduardo Rocha de Andrade, Thanh Hau Nguyen, Matheus Pedroza Ferreira, David R. Pugh
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-08-01
Series:Frontiers in Artificial Intelligence
Subjects:
graph neural network (GNN)
tensor compilation
attention mechanism
ranking loss function
machine learning for systems
Online Access:https://www.frontiersin.org/articles/10.3389/frai.2025.1605539/full
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