Multi-precision convolutional neural networks on heterogeneous hardware

Sam Amiri, Mohammad Hosseinabady, Simon McIntosh-Smith, Jose Nunez-Yanez

    Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

    11 Citations (Scopus)
    60 Downloads (Pure)


    Fully binarised convolutional neural networks (CNNs) deliver very high inference performance using single-bit weights and activations, together with XNOR type operators for the kernel convolutions. Current research shows that full binarisation results in a degradation of accuracy and different approaches to tackle this issue are being investigated such as using more complex models as accuracy reduces. This paper proposes an alternative based on a multi-precision CNN frame-work that combines a binarised and a floating point CNN in a pipeline configuration deployed on heterogeneous hardware. The binarised CNN is mapped onto an FPGA device and used to perform inference over the whole input set while the floating point network is mapped onto a CPU device and performs re-inference only when the classification confidence level is low. A light-weight confidence mechanism enables a flexible trade-off between accuracy and throughput. To demonstrate the concept, we choose a Zynq 7020 device as the hardware target and show that the multi-precision network is able to increase the BNN accuracy from 78.5% to 82.5% and the CPU inference speed from 29.68 to 90.82 images/sec.
    Original languageEnglish
    Title of host publicationDesign, Automation & Test in Europe Conference & Exhibition (DATE)
    Number of pages6
    ISBN (Electronic)978-3-9819263-0-9
    ISBN (Print)978-3-9819263-1-6
    Publication statusPublished - Mar 2018
    Event DATE - Design, Automation and Test in Europe Conference - Dresden, Germany
    Duration: 19 Mar 201823 Mar 2018

    Publication series

    NameDate Proceedings
    ISSN (Electronic)1558-1101


    Conference DATE - Design, Automation and Test in Europe Conference
    Abbreviated titleDATE 2018

    Bibliographical note

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    • multi-precision
    • performance
    • Convolutional Neural Network
    • deep learning
    • heterogeneous
    • FPGA
    • ARM
    • CIFAR-10
    • inference


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