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Binary Graph Neural Networks

Abstract : Graph Neural Networks (GNNs) have emerged as a powerful and flexible framework for representation learning on irregular data. As they generalize the operations of classical CNNs on grids to arbitrary topologies, GNNs also bring much of the implementation challenges of their Euclidean counterparts. Model size, memory footprint, and energy consumption are common concerns for many realworld applications. Network binarization allocates a single bit to parameters and activations, thus dramatically reducing the memory requirements (up to 32x compared to single-precision floating-point numbers) and maximizing the benefits of fast SIMD instructions on modern hardware for measurable speedups. However, in spite of the large body of work on binarization for classical CNNs, this area remains largely unexplored in geometric deep learning. In this paper, we present and evaluate different strategies for the binarization of graph neural networks. We show that through careful design of the models, and control of the training process, binary graph neural networks can be trained at only a moderate cost in accuracy on challenging benchmarks. In particular, we present the first dynamic graph neural network in Hamming space, able to leverage efficient k-NN search on binary vectors to speed-up the construction of the dynamic graph. We further verify that the binary models offer significant savings on embedded devices. Our code is publicly available on Github.
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https://hal.archives-ouvertes.fr/hal-03184720
Contributor : Gaétan Bahl <>
Submitted on : Monday, March 29, 2021 - 5:38:30 PM
Last modification on : Monday, June 14, 2021 - 10:25:04 AM
Long-term archiving on: : Wednesday, June 30, 2021 - 6:55:59 PM

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  • HAL Id : hal-03184720, version 1

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Mehdi Bahri, Gaétan Bahl, Stefanos Zafeiriou. Binary Graph Neural Networks. CVPR 2021 - IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Jun 2021, Nashville / Virtual, United States. ⟨hal-03184720⟩

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