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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 …
computational power and learning ability similar to the brain. However, implementation of …
A survey of neuromorphic computing and neural networks in hardware
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 …
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 …
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
We are witnessing an explosive development and widespread application of deep neural
networks (DNNs) in various fields. However, DNN models, especially a convolutional neural …
networks (DNNs) in various fields. However, DNN models, especially a convolutional neural …
An analytic formulation of convolutional neural network learning for pattern recognition
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 …
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 …
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
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 …
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 …
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
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 …
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 …
power consumption for a local industrial region (the studied case). At first, a variant of the …