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Analytic And Empirical Correction Of Biased Error Introduced By Approximation Methods
Analytic And Empirical Correction Of Biased Error Introduced By Approximation Methods
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机译：近似方法引入的有偏误差的解析和经验校正
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摘要
Various embodiments include methods and neural network computing devices implementing the methods for methods for method for generating an approximation neural network correcting for errors due to approximation operations. Various embodiments may include performing approximation operations on a weights tensor associated with a layer of a neural network to generate an approximation weights tensor, determining an expected output error of the layer in the neural network due to the approximation weights tensor, subtracting the expected output error from a bias parameter of the layer to determine an adjusted bias parameter and substituting the adjusted bias parameter for the bias parameter in the layer. Such operations may be performed for all layers in a neural network to produce an approximation version of the neural network for execution on a resource limited processor.
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