The sigmoid activation function plays an essential role in implementing neural networks in hardware. However, due to a high degree of non-linearity, the hardware implementation of the activation function itself is a daunting job. In this Letter, the authors proposed an accuracy controlled iterative algorithm for the piecewise linear approximation of the sigmoid function. The proposed algorithm approximates sigmoid activation with an optimised number of linear functions for a given error. The accuracy and hardware complexity of the resulting approximation function are compared with the state-of-the-art literature.

中文翻译：

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一种用于高效 sigmoid 函数逼近的精度控制迭代方法

sigmoid 激活函数在硬件中实现神经网络中起着至关重要的作用。然而，由于高度非线性，激活函数本身的硬件实现是一项艰巨的工作。在这封信中，作者提出了一种用于 sigmoid 函数的分段线性逼近的精度控制迭代算法。对于给定的误差，所提出的算法使用优化数量的线性函数来近似 sigmoid 激活。所得近似函数的准确性和硬件复杂性与最先进的文献进行了比较。