Error analysis of quantized weights for feedforward neural networks (FNN)

Duanpei Wu, J. Gowdy
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引用次数: 1

Abstract

When a neural network is implemented with limited precision hardware, errors from the quantization of weights become important factors to be considered. In this paper, the authors present several analysis results based on general FNN structures and use several examples to examine the relation between weight errors and output classifications. A lower bound for L, the number of bits used to quantize the weights, is derived in the worst case. This paper also includes the detailed analysis of AND-gates.<>
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前馈神经网络(FNN)量化权重误差分析
在有限精度的硬件条件下实现神经网络时,权重量化产生的误差成为需要考虑的重要因素。在本文中,作者给出了几种基于一般FNN结构的分析结果,并通过几个例子来检验权值误差与输出分类之间的关系。在最坏的情况下,得到L的下界,即用于量化权重的比特数。本文还对与门进行了详细的分析
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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