Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices,
and models that contrast the pervasive von Neumann computer architecture. This …

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 …

Recently, enormous datasets have made power dissipation and area usage lie at the heart
of designs for artificial neural networks (ANNs). Considering the significant role of activation …

Transcendental functions are commonly used in many fields such as nonlinear functions of
artificial neural networks (ANNs). Due to nonlinearity of these functions, hardware …

Despite many decades of research on high-performance Deep Neural Network (DNN)
accelerators, their massive computational demand still requires resource-efficient, optimized …

In recent years there has been a growing interest in hardware neural networks, which
express many benefits over conventional software models, mainly in applications where …

The sigmoid activation function is popular in neural networks, but its complexity limits the
hardware implementation and speed. In this paper, we use curvature values to divide the …

This paper proposes a serial hardware architecture of a multilayer perceptron (MLP) neural
network for real-time wheezing detection in respiratory sounds. As an established …

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 …