Artificial neural networks in hardware: A survey of two decades of progress
This article presents a comprehensive overview of the hardware realizations of artificial
neural network (ANN) models, known as hardware neural networks (HNN), appearing in …
neural network (ANN) models, known as hardware neural networks (HNN), appearing in …
Weightless neural networks for efficient edge inference
Weightless neural networks (WNNs) are a class of machine learning model which use table
lookups to perform inference, rather than the multiply-accumulate operations typical of deep …
lookups to perform inference, rather than the multiply-accumulate operations typical of deep …
An experimental evaluation of weightless neural networks for multi-class classification
M De Gregorio, M Giordano - Applied Soft Computing, 2018 - Elsevier
WiSARD belongs to the class of weightless neural networks, and it is based on a neural
model which uses lookup tables to store the function computed by each neuron rather than …
model which uses lookup tables to store the function computed by each neuron rather than …
Fault detection and diagnosis in dynamic systems using weightless neural networks
JCM Oliveira, KV Pontes, I Sartori… - Expert Systems with …, 2017 - Elsevier
Abstract This work examines Fault Detection and Diagnosis (FDD) based on Weightless
Neural Networks (WNN) with applications in univariate and multivariate dynamic systems …
Neural Networks (WNN) with applications in univariate and multivariate dynamic systems …
Producing pattern examples from “mental” images
BPA Grieco, PMV Lima, M De Gregorio, FMG França - Neurocomputing, 2010 - Elsevier
The WiSARD (Wilkie, Stonham and Aleksander's Recognition Device) weightless neural
network model has its functionality based on the collective response of RAM-based neurons …
network model has its functionality based on the collective response of RAM-based neurons …
ULEEN: A Novel Architecture for Ultra-low-energy Edge Neural Networks
''Extreme edge” devices, such as smart sensors, are a uniquely challenging environment for
the deployment of machine learning. The tiny energy budgets of these devices lie beyond …
the deployment of machine learning. The tiny energy budgets of these devices lie beyond …
Integer self-organizing maps for digital hardware
The Self-Organizing Map algorithm has been proven and demonstrated to be a useful
paradigm for unsupervised machine learning of two-dimensional projections of …
paradigm for unsupervised machine learning of two-dimensional projections of …
Classical and superposed learning for quantum weightless neural networks
A supervised learning algorithm for quantum neural networks (QNN) based on a novel
quantum neuron node implemented as a very simple quantum circuit is proposed and …
quantum neuron node implemented as a very simple quantum circuit is proposed and …
Automated multi-label text categorization with VG-RAM weightless neural networks
In automated multi-label text categorization, an automatic categorization system should
output a label set, whose size is unknown a priori, for each document under analysis. Many …
output a label set, whose size is unknown a priori, for each document under analysis. Many …
Weightless neural models: an overview
TB Ludermir - Women in Computational Intelligence: Key Advances …, 2022 - Springer
This paper presents an overview of research in Weightless Neural Models. Weightless
Neural Networks (WNNs) do not have weighted connections between nodes. They use a …
Neural Networks (WNNs) do not have weighted connections between nodes. They use a …