  Hyperbolic tangent and Sigmoid functions are used as non-linear activation
units in the artificial and deep neural networks. Since, these networks are
computationally expensive, customized accelerators are designed for achieving
the required performance at lower cost and power. The activation function and
MAC units are the key building blocks of these neural networks. A low
complexity and accurate hardware implementation of the activation function is
required to meet the performance and area targets of such neural network
accelerators. Moreover, a scalable implementation is required as the recent
studies show that the DNNs may use different precision in different layers.
This paper presents a novel method based on trigonometric expansion properties
of the hyperbolic function for hardware implementation which can be easily
tuned for different accuracy and precision requirements.
