Speeding-up convolutional neural networks: A survey

V. Lebedev, V. Lempitsky

    Research output: Contribution to journalArticlepeer-review

    19 Citations (Scopus)

    Abstract

    Convolutional neural networks (CNN) have become ubiquitous in computer vision as well as several other domains, but the sheer size of the modern CNNs means that for the majority of practical applications, a significant speed up and compression are often required. Speeding-up CNNs therefore have become a very active area of research with multiple diverse research directions pursued by many groups in academia and industry. In this short survey, we cover several research directions for speeding up CNNs that have become popular recently. Specifically, we cover approaches based on tensor decompositions, weight quantization, weight pruning, and teacher-student approaches. We also review CNN architectures designed for optimal speed and briefly consider automatic architecture search.

    Original languageEnglish
    Pages (from-to)799-810
    Number of pages12
    JournalBulletin of the Polish Academy of Sciences: Technical Sciences
    DOIs
    Publication statusPublished - 2018

    Keywords

    • Algorithm optimization
    • Convolutional neural networks
    • Resource-efficient computation

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