Deep neural networks yield the state-of-the-art results in many computer vision and human machine interface applications such as object detection, speech recognition etc. Since, these networks are computationally expensive, customized accelerators are designed for achieving the required performance at lower cost and power. One of the key building blocks of these neural networks is non-linear activation function such as sigmoid, hyperbolic tangent (tanh), and ReLU. A low complexity accurate hardware implementation of the activation function is required to meet the performance and area targets of the neural network accelerators. Even though, various methods and implementations of tanh activation function have been published, a comparative study is missing. This paper presents comparative analysis of polynomial and rational methods and their hardware implementation.

翻译：深神经网络在许多计算机视觉和物体探测、语音识别等人体机器界面应用中产生最先进的结果。由于这些网络计算成本昂贵,因此设计了定制加速器,以较低成本和功率达到要求的性能。这些神经网络的关键组成部分之一是非线性激活功能,如硅状、双曲采光(tanh)和RELU。激活功能的精确硬件实施要达到神经网络加速器的性能和面积目标,需要使用低复杂度的硬件。尽管已经公布了各种土制激活功能的方法和实施方法,但缺少一项比较研究。本文对多种和合理方法及其硬件实施进行了比较分析。