Neuromorphic Devices, Circuits, Systems and Their Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Circuit and Signal Processing".

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 1288

Special Issue Editors


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Guest Editor
Department of Electronics, University of Valladolid, Paseo Belén 15, 47011 Valladolid, Spain
Interests: RRAM; neuromorphic computing; memristors; multilevel control; high-k dielectrics; electrical characterization
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Guest Editor
Department of Informatics, University of Valladolid, Paseo Belén 15, 47011 Valladolid, Spain
Interests: computing architecture, neuronal networks; deep-learning

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Guest Editor
Department of Electronics, University of Valladolid, Paseo Belén 15, 47011 Valladolid, Spain
Interests: electronic materials and devices; memristive materials; electrical characterization

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to bring together the latest advances in neuromorphic computing. Emerging devices and architectures as well as the latest advances based on conventional CMOS VLSI technologies will be addressed. Special emphasis will be given to solutions that overcome the bottleneck of von Neumann architectures.

Bio-inspired systems will also be considered: architectures that emulate biological systems and represent important advances in artificial intelligence and deep learning.

Advances in materials and devices with behavior similar to the constituent elements of the human brain (synapses, neurons, etc.) are relevant aspects that will be included in this Special Issue.

Researchers are invited to send their contributions in the following topics:

(1) Materials and devices: memristors, synaptic devices, etc.

(2) Neuromorphic circuits

(3) Neuromorphic computer architectures: beyond von Neumann architecture

(4) Deep neural networks

(5) Spike neural networks

(6) Neuromorphic algorithms: machine-learning and non-machine learning

(7) Hardware accelerators

(8) Neuromorphic applications in different areas: health, automotive, industry, natural sciences, agriculture, astronomy, etc.

(9) Bioethics

Prof. Dr. Salvador Dueñas
Prof. Dr. Benjamín Sahelices
Prof. Dr. Héctor García
Guest Editors

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Keywords

  • memristors
  • neuromorphic network
  • brain computing
  • neuronal networks
  • machine learning
  • deep learning

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Published Papers (1 paper)

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Research

16 pages, 4089 KiB  
Article
Simultaneous Velocity and Texture Classification from a Neuromorphic Tactile Sensor Using Spiking Neural Networks
by George Brayshaw, Benjamin Ward-Cherrier and Martin J. Pearson
Electronics 2024, 13(11), 2159; https://doi.org/10.3390/electronics13112159 - 1 Jun 2024
Viewed by 871
Abstract
The neuroTac, a neuromorphic visuo-tactile sensor that leverages the high temporal resolution of event-based cameras, is ideally suited to applications in robotic manipulators and prosthetic devices. In this paper, we pair the neuroTac with Spiking Neural Networks (SNNs) to achieve a movement-invariant neuromorphic [...] Read more.
The neuroTac, a neuromorphic visuo-tactile sensor that leverages the high temporal resolution of event-based cameras, is ideally suited to applications in robotic manipulators and prosthetic devices. In this paper, we pair the neuroTac with Spiking Neural Networks (SNNs) to achieve a movement-invariant neuromorphic tactile sensing method for robust texture classification. Alongside this, we demonstrate the ability of this approach to extract movement profiles from purely tactile data. Our systems achieve accuracies of 95% and 83% across their respective tasks (texture and movement classification). We then seek to reduce the size and spiking activity of our networks with the aim of deployment to edge neuromorphic hardware. This multi-objective optimisation investigation using Pareto frontiers highlights several design trade-offs, where high activity and large network sizes can both be reduced by up to 68% and 94% at the cost of slight decreases in accuracy (8%). Full article
(This article belongs to the Special Issue Neuromorphic Devices, Circuits, Systems and Their Applications)
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