Papers › Graph neural networks with configuration cross-attention for tensor compilers

Graph neural networks with configuration cross-attention for tensor compilers

26 May 2024arXiv:2405.16623archive 2025-07-28

Dmitrii Khizbullin, Eduardo Rocha de Andrade, Thanh Hau Nguyen, Matheus Pedroza Ferreira, David R. Pugh

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 combinatorially large number of ways, some configurations leading to accelerated inference. We propose TGraph, a neural graph architecture that allows screening for fast configurations of the target computational graph, thus representing an artificial intelligence (AI) tensor compiler in contrast to the traditional heuristics-based compilers. The proposed solution improves mean Kendall's τ across layout collections of TpuGraphs from 29.8% of the reliable baseline to 67.4% of TGraph. We estimate the potential CO₂ emission reduction associated with our work to be equivalent to over 50% of the total household emissions in the areas hosting AI-oriented data centers.

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Code

thanhhau097/google_fast_or_slow officialmentioned in paperpytorch report

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Tasks

Graph Property PredictionGraph RegressionRuntime ranking

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Runtime ranking TpuGraphs Layout mean TGraph Kendall's Tau 0.674 #1 of 2 Archive leaderboard report

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Methods

GraphSAGE

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