Efficient Hardware Implementation of the Tanh Activation Function Using Non-Linear Sampling for Deep Learning Architectures

Activation functions play a central role in deep learning models, directly influencing network performance and training stability. Among them, the hyperbolic tangent (tanh) function is widely used in convolutional neural networks (CNNs), hybrid architectures, and as a core component of LSTM networks. However, implementing the tanh function in hardware presents a major challenge due to its non-linear nature and computational complexity.This paper introduces an efficient hardware implementation of the tanh function based on a non-linear sampling strategy. The proposed method samples ۲۵۰ points from the tanh curve, with denser sampling in the central region and sparser points at the tails. To compute the tanh value of any input, a simple linear interpolation is performed between the closest two sampled points. This method offers a balance between computational efficiency and accuracy.The architecture is implemented in VHDL, using a ۳۲-bit signed fixed-point representation for both input and output. Despite its simplicity, the approach maintains sufficient precision for use in deep neural networks and is optimized for environments with limited resources. Simulation results confirm the accuracy of the method, showing close agreement with standard mathematical computations. The implementation is highly suitable for deployment on FPGAs, offering high performance with minimal hardware overhead.

کلیدواژه ها:

FPGA ، Tanh ، VHDL ، Activation Function