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EuclidNets: An Alternative Operation for Efficient Inference of Deep Learning Models

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Abstract

With the advent of deep learning applications on edge devices, researchers actively try to optimize deep learning model deployment on low-power and restricted memory devices. There are established compression methods such as quantization, pruning, and architecture search that leverage commodity hardware. Apart from conventional compression algorithms, one may redesign the operations of deep learning models, leading to more efficient hardware implementation. To this end, we propose EuclidNet, an efficient computing method designed to be implemented on hardware that replaces multiplication, with squared difference. We show that EuclidNet is aligned with matrix multiplication and can be used as a measure of similarity in the case of convolutional layers. Furthermore, we show that under various transformations and noise scenarios, EuclidNet exhibits the same performance compared to the deep learning models designed with multiplication operations.

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Authors and Affiliations

  1. Huawei Noah’s Ark Lab, Montreal Research Centre, 7101 Park Avenue, Montreal, H3N 1X9, QC, Canada

    Xinlin Li, Alireza Ghaffari & Vahid Partovi Nia

  2. Department of Mathematics and Statistics, McGill University, 805 Sherbrooke Street West, Montreal, H3A 0B9, QC, Canada

    Mariana Parazeres, Adam Oberman & Masoud Asgharian

  3. Department of Mathematics and Industrial Engineering, Polytechnique Montreal, 2500 Chem. Polytechnique, Montreal, H3T 1J4, QC, Canada

    Vahid Partovi Nia

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  1. Xinlin Li
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This article is part of the topical collection “Advances on Pattern Recognition Applications and Methods 2022” guest edited by Ana Fred, Maria De Marsico and Gabriella Sanniti di Baja.

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Li, X., Parazeres, M., Oberman, A. et al. EuclidNets: An Alternative Operation for Efficient Inference of Deep Learning Models. SN COMPUT. SCI. 4, 507 (2023). https://doi.org/10.1007/s42979-023-01921-y

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