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 …

Computational intelligence is employed to solve factual and complicated global problems,
though neural networks (NNs) and evolutionary computing have also affected these issues …

Convolutional Neural Networks (CNN) continue to dominate research in the area of
hardware acceleration using Field Programmable Gate Arrays (FPGA), proving its …

New chips for machine learning applications appear, they are tuned for a specific topology,
being efficient by using highly parallel designs at the cost of high power or large complex …

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 work, we propose an efficient architecture for the hardware realization of deep neural
networks on reconfigurable computing platforms like FPGA. The proposed neural network …

With the continuous expansion of computer network scale and the diversity of network
equipment, the traditional single computer network maintenance and management method …

The most active research area for Field Programmable Gate Arrays is the Convolution
Neural Network (CNN), and the gist of any CNN is an activation function. Therefore, various …

A new learning machine based on neural network (NN) and its hardware accelerator are
successfully built in this study for predicting the luminance decay of Organic Light Emitting …

Abstract Weightless Neural Networks (WNNs) are a powerful mechanism for pattern
recognition. Aiming at enhancing their learning capabilities, Multi-valued Probabilistic Logic …