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Video s3
    Details
    Presenter(s)
    Qingyu Guo Headshot
    Display Name
    Qingyu Guo
    Affiliation
    Affiliation
    Peking University
    Country
    Author(s)
    Display Name
    Qingyu Guo
    Affiliation
    Affiliation
    Peking University
    Display Name
    Xiaoxin Cui
    Affiliation
    Affiliation
    Peking University
    Display Name
    Jian Zhang
    Affiliation
    Affiliation
    Beijing Zhicun WITIN Technology Corporation Limited
    Display Name
    Aifei Zhang
    Affiliation
    Affiliation
    Beijing Zhicun WITIN Technology Corporation Limited
    Display Name
    Xinjie Guo
    Affiliation
    Affiliation
    Beijing Zhicun WITIN Technology Corporation Limited
    Display Name
    Yuan Wang
    Affiliation
    Affiliation
    Peking University
    Abstract

    We proposed an integer-only quantization method. With no division or big integer multiplication, this quantization method is suitable to be deployed on co-designed hardware platforms. We applied 4 bit quantization on some classical networks. On MNIST, CIFAR10 and CIFAR100, quantization networks perform even better than original networks. On SpeechCommands, quantization error is 0.16%. We also deployed quantized networks on a flash-based in-memory-computing chip to verify this method’s feasibility.

    Slides
    • A 4-Bit Integer-Only Neural Network Quantization Method Based on Shift Batch Normalization (application/pdf)