2014/01/01 by Babak Zamanlooy, Mitra Mirhassani
Computer Science · Engineering · #Advanced Memory and Neural Computing #Analog and Mixed-Signal Circuit Design #Neural Networks and Applications
paper · doi:10.1109/tvlsi.2012.2232321
openalex publication_date 2014/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
Nonlinear activation function is one of the main building blocks of artificial neural networks. Hyperbolic tangent and sigmoid are the most used nonlinear activation functions. Accurate implementation of these transfer functions in digital networks faces certain challenges. In this paper, an efficient approximation scheme for hyperbolic tangent function is proposed. The approximation is based on a mathematical analysis considering the maximum allowable error as design parameter. Hardware implementation of the proposed approximation scheme is presented, which shows that the proposed structure compares favorably with previous architectures in terms of area and delay. The proposed structure requires less output bits for the same maximum allowable error when compared to the state-of-the-art. The number of output bits of the activation function determines the bit width of multipliers and adders in the network. Therefore, the proposed activation function results in reduction in area, delay, and power in VLSI implementation of artificial neural networks with hyperbolic tangent activation function.