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FIXED POINT NEURAL NETWORK BASED ON FLOATING POINT NEURAL NETWORK QUANTIZATION

机译:基于浮点神经网络量化的不动点神经网络

摘要

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.
机译:一种使用量化器对浮点机器学习网络进行量化以获得固定点机器学习网络的方法,可以包括选择浮点机器学习网络的输入分布的至少一个矩。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所选时刻,确定用于量化浮点机器学习网络的值的量化器参数,以获得定点机器的对应值。学习网络。

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