A comprehensive review on the problem of choosing a suitable activation function for the
hidden layer of a feed forward neural network has been widely investigated. Since the …

The field of wireless communication is currently being pushed to new boundaries with the
emergence of 6G technology. This advanced technology requires substantially increased …

In this brief, a novel approximation method and its optimized hardware implementation are
proposed for the sigmoid function used in Deep Neural Networks (DNNs). Based on …

This brief presents the hardware implementation of deep neural network-based speech
enhancement algorithm (DNN-SEA) with a precise sigmoid activation function. Further, an …

The echo state network (ESN) is a recently developed machine-learning paradigm whose
processing capabilities rely on the dynamical behavior of recurrent neural networks. Its …

5G and beyond 5G wireless communication has revolutionized our daily lives. However, the
increased bandwidth and data rate transfer in 5G present challenges, particularly in the …

In this paper we propose a low-error approximation of the sigmoid function and hyperbolic
tangent, which are mainly used to activate the artificial neuron, based on the piecewise …

This paper presents the high accuracy hardware implementation of the hyperbolic tangent
and sigmoid activation functions for artificial neural networks. A kind of a direct …

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 …

Hardware implementations of spiking neural networks, which are known as neuromorphic
architectures, provide an explicit understanding of brain performance. As a result, biological …