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Electronics
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Bio-Inspired Architectures: From Neuroscience to Embedded AI
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Bio-Inspired Architectures: From Neuroscience to Embedded AI

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 6561

Special Issue Editors

Dr. Andres Upegui

E-Mail Website
Guest Editor
inIT, Hepia, University of Applied Sciences of Western Switzerland, 1202 Geneva, Switzerland
Interests: reconfigurable computing; self-adaptive hardware; neuromorphic architectures
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Bernard Girau

E-Mail Website
Guest Editor
Faculty of Sciences and Technologies, University of Lorraine, Lorraine, France
Interests: embedded parallel connectionism; bio-inspired neural models for visual perception
Prof. Dr. Benoît Miramond

E-Mail Website
Guest Editor
LEAT, Université Côte d'Azur, 06903 Sophia Antipolis, France
Interests: electronic/hardware; bio-inspired computing; spiking neural networks; event-based processing; neuromorphic architectures; embedded AI
Special Issues, Collections and Topics in MDPI journals
Dr. Nicolas Rougier

E-Mail Website
Guest Editor
Inria Bordeaux Sud-Ouest, Talence, France - Institut des Maladies Neurodégénératives, Bordeaux, France – Laboratoire Bordelais d’Informatique, Talence, France – Université de Bordeaux, Bordeaux, France
Interests: neuroscience; cognition; enaction; behavior; embodiment; learning; model; decision making; distributed computing; neural networks; machine learning; Artificial Intelligence

Special Issue Information

Dear Colleagues,

For several decades, engineers have used inspiration from biology to propose solutions in diverse domains ranging from mechanics to computer sciences. Computer architecture design has not been an exception, as biology has inspired hardware design in several ways. Bio-inspiration can be found in the organization of hardware systems in order to perform computation, and it can be found as neuromorphic or cellular hardware architectures, among others. Another form of bio-inspiration can be identified in the form of features present in living beings, which are of particular interest for engineered systems. Evolvability, self-organization, fault tolerance, adaptivity, and learning are only some examples of features that can enhance the capabilities of embedded computer architectures in a significant manner.

This Special Issue aims to compile recent works on architectures, methods, and bio-inspired feature implementations. Potential topics include but are not limited to the following:

  • Neuromorphic architectures;
  • Self-organizing hardware;
  • Evolvable and adaptive hardware;
  • Self-reconfigurable hardware;
  • Self-repairing and fault-tolerant systems;
  • Real use-cases of bio-inspired hardware;
  • Run-time hardware evolution;
  • Run-time learning strategies.

Prof. Dr. Andres Upegui
Prof. Dr. Bernard Girau
Prof. Dr. Benoît Miramond
Dr. Nicolas Rougier
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (2 papers)

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Research

20 pages, 4670 KiB  
Article
Hardware Architecture for Asynchronous Cellular Self-Organizing Maps
by Quentin Berthet, Joachim Schmidt and Andres Upegui
Electronics 2022, 11(2), 215; https://doi.org/10.3390/electronics11020215 - 11 Jan 2022
Viewed by 1386
Abstract
Nowadays, one of the main challenges in computer architectures is scalability; indeed, novel processor architectures can include thousands of processing elements on a single chip and using them efficiently remains a big issue. An interesting source of inspiration for handling scalability is the [...] Read more.
Nowadays, one of the main challenges in computer architectures is scalability; indeed, novel processor architectures can include thousands of processing elements on a single chip and using them efficiently remains a big issue. An interesting source of inspiration for handling scalability is the mammalian brain and different works on neuromorphic computation have attempted to address this question. The Self-configurable 3D Cellular Adaptive Platform (SCALP) has been designed with the goal of prototyping such types of systems and has led to the proposal of the Cellular Self-Organizing Maps (CSOM) algorithm. In this paper, we present a hardware architecture for CSOM in the form of interconnected neural units with the specific property of supporting an asynchronous deployment on a multi-FPGA 3D array. The Asynchronous CSOM (ACSOM) algorithm exploits the underlying Network-on-Chip structure to be provided by SCALP in order to overcome the multi-path propagation issue presented by a straightforward CSOM implementation. We explore its behaviour under different map topologies and scalar representations. The results suggest that a larger network size with low precision coding obtains an optimal ratio between algorithm accuracy and FPGA resources. Full article
(This article belongs to the Special Issue Bio-Inspired Architectures: From Neuroscience to Embedded AI)
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32 pages, 15152 KiB  
Article
Brain-Inspired Self-Organization with Cellular Neuromorphic Computing for Multimodal Unsupervised Learning
by Lyes Khacef, Laurent Rodriguez and Benoît Miramond
Electronics 2020, 9(10), 1605; https://doi.org/10.3390/electronics9101605 - 1 Oct 2020
Cited by 10 | Viewed by 4264
Abstract
Cortical plasticity is one of the main features that enable our ability to learn and adapt in our environment. Indeed, the cerebral cortex self-organizes itself through structural and synaptic plasticity mechanisms that are very likely at the basis of an extremely interesting characteristic [...] Read more.
Cortical plasticity is one of the main features that enable our ability to learn and adapt in our environment. Indeed, the cerebral cortex self-organizes itself through structural and synaptic plasticity mechanisms that are very likely at the basis of an extremely interesting characteristic of the human brain development: the multimodal association. In spite of the diversity of the sensory modalities, like sight, sound and touch, the brain arrives at the same concepts (convergence). Moreover, biological observations show that one modality can activate the internal representation of another modality when both are correlated (divergence). In this work, we propose the Reentrant Self-Organizing Map (ReSOM), a brain-inspired neural system based on the reentry theory using Self-Organizing Maps and Hebbian-like learning. We propose and compare different computational methods for unsupervised learning and inference, then quantify the gain of the ReSOM in a multimodal classification task. The divergence mechanism is used to label one modality based on the other, while the convergence mechanism is used to improve the overall accuracy of the system. We perform our experiments on a constructed written/spoken digits database and a Dynamic Vision Sensor (DVS)/EletroMyoGraphy (EMG) hand gestures database. The proposed model is implemented on a cellular neuromorphic architecture that enables distributed computing with local connectivity. We show the gain of the so-called hardware plasticity induced by the ReSOM, where the system’s topology is not fixed by the user but learned along the system’s experience through self-organization. Full article
(This article belongs to the Special Issue Bio-Inspired Architectures: From Neuroscience to Embedded AI)
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