Hardware implementation of Spiking Neural Network classifiers based on backpropagation-based learning algorithms

2009 ◽  
Author(s):  
Marco Aurelio Nuno-Maganda ◽  
Miguel Arias-Estrada ◽  
Cesar Torres-Huitzil ◽  
Bernard Girau
Keyword(s):  
Neural Network ◽  
Hardware Implementation ◽  
Learning Algorithms ◽  
Spiking Neural Network ◽  
Neural Network Classifiers

Neuromorphic Memristive Chips: Design and Technology

2021 ◽  
Vol 23 (6) ◽  
pp. 285-294
Author(s):  
N.V. Andreeva ◽  
◽  
V.V. Luchinin ◽  
E.A. Ryndin ◽  
M.G. Anchkov ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Resistive Switching ◽  
Hardware Implementation ◽  
Functional Materials ◽  
Design And Technology ◽  
Spiking Neural Network ◽  
Chip Design ◽  
Neuromorphic Hardware ◽  
Metal Insulator Metal

Memristive neuromorphic chips exploit a prospective class of novel functional materials (memristors) to deploy a new architecture of spiking neural networks for developing basic blocks of brain-like systems. Memristor-based neuromorphic hardware solutions for multi-agent systems are considered as challenges in frontier areas of chip design for fast and energy-efficient computing. As functional materials, metal oxide thin films with resistive switching and memory effects (memristive structures) are recognized as a potential elemental base for new components of neuromorphic engineering, enabling a combination of both data storage and processing in a single unit. A key design issue in this case is a hardware defined functionality of neural networks. The gradient change of resistive properties of memristive elements and its non-volatile memory behavior ensure the possibility of spiking neural network organization with unsupervised learning through hardware implementation of basic synaptic mechanisms, such as Hebb's learning rules including spike — timing dependent plasticity, long-term potentiation and depression. This paper provides an overview of scientific researches carrying out at Saint Petersburg Electrotechnical University "LETI" since 2014 in the field of novel electronic components for neuromorphic hardware solutions of brain-like chip design. Among the most promising concepts developed by ETU "LETI" are: the design of metal-insulator-metal structures exhibiting multilevel resistive switching (gradient tuning of resistive properties and bipolar resistive switching are combined together in a sin¬gle memristive element) for further use as artificial synaptic devices in neuromorphic chips; computing schemes for spatio-temporal pattern recognition based on spiking neural network architecture implementation; breadboard models of analogue circuits of hardware implementation of neuromorphic blocks for brain-like system developing.

scholarly journals Quadrupedal Robot Locomotion: A Biologically Inspired Approach and Its Hardware Implementation

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
A. Espinal ◽  
H. Rostro-Gonzalez ◽  
M. Carpio ◽  
E. I. Guerra-Hernandez ◽  
M. Ornelas-Rodriguez ◽  
...  
Keyword(s):  
Neural Network ◽  
Hardware Implementation ◽  
Pattern Generator ◽  
Quadruped Robot ◽  
Spiking Neural Network ◽  
Resource Usage ◽  
Hexapod Robot ◽  
Biologically Inspired ◽  
Robot Locomotion ◽  
Locomotion System

A bioinspired locomotion system for a quadruped robot is presented. Locomotion is achieved by a spiking neural network (SNN) that acts as a Central Pattern Generator (CPG) producing different locomotion patterns represented by their raster plots. To generate these patterns, the SNN is configured with specific parameters (synaptic weights and topologies), which were estimated by a metaheuristic method based on Christiansen Grammar Evolution (CGE). The system has been implemented and validated on two robot platforms; firstly, we tested our system on a quadruped robot and, secondly, on a hexapod one. In this last one, we simulated the case where two legs of the hexapod were amputated and its locomotion mechanism has been changed. For the quadruped robot, the control is performed by the spiking neural network implemented on an Arduino board with 35% of resource usage. In the hexapod robot, we used Spartan 6 FPGA board with only 3% of resource usage. Numerical results show the effectiveness of the proposed system in both cases.

An efficient hardware implementation of a novel unary Spiking Neural Network multiplier with variable dendritic delays

2016 ◽  
Vol 189 ◽  
pp. 130-134 ◽  
Author(s):  
Carlos Diaz ◽  
Giovanny Sanchez ◽  
Gonzalo Duchen ◽  
Mariko Nakano ◽  
Hector Perez
Keyword(s):  
Neural Network ◽  
Hardware Implementation ◽  
Spiking Neural Network

A low-cost and high-speed hardware implementation of spiking neural network

2020 ◽  
Vol 382 ◽  
pp. 106-115 ◽  
Author(s):  
Guohe Zhang ◽  
Bing Li ◽  
Jianxing Wu ◽  
Ran Wang ◽  
Yazhu Lan ◽  
...  
Keyword(s):  
Neural Network ◽  
High Speed ◽  
Hardware Implementation ◽  
Low Cost ◽  
Spiking Neural Network

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

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