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 …

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 …

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

In this paper, we present an efficient design of lookup-table (LUT) for the evaluation of
hyperbolic tangent sigmoid to be used for the hardware implementation of artificial neural …

This paper discusses the artificial neural network (ANN) implementation into a field
programmable gate array (FPGA). One of the most difficult problem encounters is the …

In this paper, the design of a single neuron which contains a sigmoid activation function was
proposed and implemented using the FPGAs (Field Programmable Gate Array) techniques …

Implementations of machine learning models in resource-limited embedded systems are
becoming highly desired. This has led to a need for resource-efficient building blocks for …

The complex equation of sigmoid function is one of the most difficult problems encountered
for implementing the artificial neural network (ANN) into a field programmable gate array …

Adders are an almost requisite element of every modern integrated circuit. As in the current
era of increasing digitalization, where everyone works towards miniaturization, three …

In this paper, we present the efficient hardware implementation of hyperbolic tangent
activation function, which is most widely used in artificial neural networks for accelerating …