Summary of Graph Neural Networks with Configuration Cross-attention For Tensor Compilers, by Dmitrii Khizbullin et al.
Graph neural networks with configuration cross-attention for tensor compilers
by Dmitrii Khizbullin, Eduardo Rocha de Andrade, Thanh Hau Nguyen, Matheus Pedroza Ferreira, David R. Pugh
First submitted to arxiv on: 26 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Hardware Architecture (cs.AR); Performance (cs.PF)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed solution, TGraph, is a neural graph architecture that accelerates neural network inference by efficiently screening for fast configurations of the target computational graph. By representing the neural network as a graph with nodes as operators transforming multidimensional tensors, TGraph improves mean Kendall’s τ across layout collections from 29.8% to 67.4% compared to traditional heuristics-based compilers. The potential CO2 emission reduction associated with this work is estimated to be equivalent to over 50% of the total household emissions in areas hosting AI-oriented data centers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TGraph helps speed up how computers do math for artificial intelligence (AI) tasks. It finds the best way to make calculations faster by looking at lots of different ways to organize the information. This makes it better than old methods that just guessed what would work best. This is important because AI uses a lot of energy and making it more efficient can help reduce pollution. |
Keywords
» Artificial intelligence » Inference » Neural network
