Neuromorphic computing with memristor crossbar

X Zhang, A Huang, Q Hu, Z **ao… - physica status solidi (a …, 2018 - Wiley Online Library
Neural networks, one of the key artificial intelligence technologies today, have the
computational power and learning ability similar to the brain. However, implementation of …

A survey of neuromorphic computing and neural networks in hardware

CD Schuman, TE Potok, RM Patton, JD Birdwell… - arxiv preprint arxiv …, 2017 - arxiv.org
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 …

[HTML][HTML] LPWAN and embedded machine learning as enablers for the next generation of wearable devices

R Sanchez-Iborra - Sensors, 2021 - mdpi.com
The penetration of wearable devices in our daily lives is unstoppable. Although they are very
popular, so far, these elements provide a limited range of services that are mostly focused …

Quantized CNN: A unified approach to accelerate and compress convolutional networks

J Cheng, J Wu, C Leng, Y Wang… - IEEE transactions on …, 2017 - ieeexplore.ieee.org
We are witnessing an explosive development and widespread application of deep neural
networks (DNNs) in various fields. However, DNN models, especially a convolutional neural …

An analytic formulation of convolutional neural network learning for pattern recognition

H Zhuang, Z Lin, Y Yang, KA Toh - Information Sciences, 2025 - Elsevier
Training convolutional neural networks (CNNs) using back-propagation (BP) is a time-
consuming and resource-intensive process, primarily due to the need to iterate over the …

A novel systolic parallel hardware architecture for the FPGA acceleration of feedforward neural networks

LD Medus, T Iakymchuk, JV Frances-Villora… - IEEE …, 2019 - ieeexplore.ieee.org
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 …

A low cost neuromorphic learning engine based on a high performance supervised SNN learning algorithm

A Siddique, MI Vai, SH Pun - Scientific Reports, 2023 - nature.com
Spiking neural networks (SNNs) are more energy-and resource-efficient than artificial neural
networks (ANNs). However, supervised SNN learning is a challenging task due to non …

Layer multiplexing FPGA implementation for deep back-propagation learning

F Ortega-Zamorano, JM Jerez… - Integrated Computer …, 2017 - content.iospress.com
Training of large scale neural networks, like those used nowadays in Deep Learning
schemes, requires long computational times or the use of high performance computation …

An enhanced fuzzy controller based on improved genetic algorithm for speed control of DC motors

A Lotfy, M Kaveh, MR Mosavi, AR Rahmati - Analog Integrated Circuits and …, 2020 - Springer
Because of being imprecision and existence of uncertainty in input variables to fuzzy
systems, and also their easy implementation, fuzzy controllers are introduced as one of …

Advanced AI-based techniques to predict daily energy consumption: A case study

A Baba - Expert Systems with Applications, 2021 - Elsevier
In this paper, we compare the efficiency of three different techniques used to predict the daily
power consumption for a local industrial region (the studied case). At first, a variant of the …