Graph neural networks with configuration cross-attention for tensor compilers

• URL: http://arxiv.org/abs/2405.16623v2
• Date: Mon, 25 Nov 2024 13:25:16 GMT
• Title: Graph neural networks with configuration cross-attention for tensor compilers
• Authors: Dmitrii Khizbullin, Eduardo Rocha de Andrade, Thanh Hau Nguyen, Matheus Pedroza Ferreira, David R. Pugh,
• Abstract summary: We propose TGraph, a neural graph architecture that allows screening for fast configurations of the target computational graph. We estimate the potential CO$ emission reduction associated with our work to be equivalent to over 50% of the total household emissions in areas hosting AI-oriented data centers.
• Score: 0.157286095422595
• License:
• Abstract: 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 $\tau$ across layout collections of TpuGraphs from 29.8% of the reliable baseline to 67.4% of TGraph. We estimate the potential CO$_2$ 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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