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    REDUCED COMPUTATIONAL COMPLEXITY FOR FIXED POINT NEURAL NETWORK
    1.
    发明申请
    REDUCED COMPUTATIONAL COMPLEXITY FOR FIXED POINT NEURAL NETWORK 审中-公开
    固定点神经网络的降低计算复杂度

    公开(公告)号：US20160328645A1

    公开(公告)日：2016-11-10

    申请号：US14882351

    申请日：2015-10-13

    Applicant: QUALCOMM Incorporated

    Inventor： Dexu LIN ,  Matthew BADIN ,  David Edward HOWARD ,  Daniel Hendricus Franciscus DIJKMAN ,  Michael Colin TREMAINE ,  Anthony SARAH

    IPC: G06N3/08 ,  G06N99/00

    CPC classification number: G06N3/08 ,  G06N3/063 ,  G06N20/00

    Abstract: A method of reducing computational complexity for a fixed point neural network operating in a system having a limited bit width in a multiplier-accumulator (MAC) includes reducing a number of bit shift operations when computing activations in the fixed point neural network. The method also includes balancing an amount of quantization error and an overflow error when computing activations in the fixed point neural network.

    Abstract translation: 一种降低在乘法器 - 累加器（MAC）中具有有限位宽度的系统中操作的固定点神经网络的计算复杂度的方法包括在计算固定点神经网络中的激活时减少多个位移操作。 该方法还包括在计算固定点神经网络中的激活时平衡量化误差量和溢出误差。

    FIXED POINT NEURAL NETWORK BASED ON FLOATING POINT NEURAL NETWORK QUANTIZATION
    2.
    发明申请
    FIXED POINT NEURAL NETWORK BASED ON FLOATING POINT NEURAL NETWORK QUANTIZATION 审中-公开
    基于浮动点神经网络定量的固定点神经网络

    公开(公告)号：US20160328646A1

    公开(公告)日：2016-11-10

    申请号：US14920099

    申请日：2015-10-22

    Applicant: QUALCOMM Incorporated

    Inventor： Dexu LIN ,  Venkata Sreekanta Reddy ANNAPUREDDY ,  David Edward HOWARD ,  David Jonathan JULIAN ,  Somdeb MAJUMDAR ,  William Richard BELL, II

    IPC: G06N3/08 ,  G06N3/04

    CPC classification number: G06N3/08 ,  G06K9/4628 ,  G06N3/04 ,  G06N3/06 ,  G06N3/10

    Abstract: A method of quantizing a floating point machine learning network to obtain a fixed point machine learning network using a quantizer may include selecting at least one moment of an input distribution of the floating point machine learning network. The method may also include determining quantizer parameters for quantizing values of the floating point machine learning network based at least in part on the at least one selected moment of the input distribution of the floating point machine learning network to obtain corresponding values of the fixed point machine learning network.

    Abstract translation: 使用量化器来量化浮点计算机学习网络以获得定点机器学习网络的方法可以包括选择浮点计算机学习网络的输入分布的至少一个时刻。 该方法还可以包括：至少部分地基于浮点机器学习网络的输入分布的至少一个选定时刻来确定用于量化浮点计算机学习网络的值的量化器参数，以获得固定点计算机的相应值 学习网络

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