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Applied Sciences
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Recent Advances in Computational Intelligence: From Theories to Applications
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Recent Advances in Computational Intelligence: From Theories to Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 3345

Special Issue Editor

Prof. Dr. Ernesto Sanchez

E-Mail Website
Guest Editor
Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy
Interests: microprocessor reliability; ANN reliability; CAD; bioinspired heuristics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Computational intelligence-based applications are occupying an always larger space in our normal lives due to their computational capabilities and outstanding results. These applications have been exploited in very different environments: from autonomous driving to medical solutions, from financing to cybersecurity, also including space applications and home appliances. However, there is a lack of a holistic analysis and comprehension of the reliability issues that may affect computational intelligence applications. For example, in most cases, it is necessary to start defining the used fault models to present a failure analysis from the software or hardware points of view. On the other side, the metrics to correctly assess the reliability of computational intelligence applications are not well defined. The research community is still investigating the most appropriate mitigation strategies to improve application reliability.

Authors and research groups exploring solutions to the mentioned issues are very welcome to send their original contributions to the proposed Special Issue.

Prof. Dr. Ernesto Sanchez
Guest Editor

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. Applied Sciences 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.

Keywords

  • Computational intelligence applications
  • reliability
  • fault-tolerant
  • artificial neural networks
  • artificial intelligence
  • mitigation strategies

Published Papers (1 paper)

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Research

27 pages, 1513 KiB  
Article
On the Reliability Assessment of Artificial Neural Networks Running on AI-Oriented MPSoCs
by Annachiara Ruospo and Ernesto Sanchez
Appl. Sci. 2021, 11(14), 6455; https://doi.org/10.3390/app11146455 - 13 Jul 2021
Cited by 16 | Viewed by 2661
Abstract
Nowadays, the usage of electronic devices running artificial neural networks (ANNs)-based applications is spreading in our everyday life. Due to their outstanding computational capabilities, ANNs have become appealing solutions for safety-critical systems as well. Frequently, they are considered intrinsically robust and fault tolerant [...] Read more.
Nowadays, the usage of electronic devices running artificial neural networks (ANNs)-based applications is spreading in our everyday life. Due to their outstanding computational capabilities, ANNs have become appealing solutions for safety-critical systems as well. Frequently, they are considered intrinsically robust and fault tolerant for being brain-inspired and redundant computing models. However, when ANNs are deployed on resource-constrained hardware devices, single physical faults may compromise the activity of multiple neurons. Therefore, it is crucial to assess the reliability of the entire neural computing system, including both the software and the hardware components. This article systematically addresses reliability concerns for ANNs running on multiprocessor system-on-a-chips (MPSoCs). It presents a methodology to assign resilience scores to individual neurons and, based on that, schedule the workload of an ANN on the target MPSoC so that critical neurons are neatly distributed among the available processing elements. This reliability-oriented methodology exploits an integer linear programming solver to find the optimal solution. Experimental results are given for three different convolutional neural networks trained on MNIST, SVHN, and CIFAR-10. We carried out a comprehensive assessment on an open-source artificial intelligence-based RISC-V MPSoC. The results show the reliability improvements of the proposed methodology against the traditional scheduling. Full article
(This article belongs to the Special Issue Recent Advances in Computational Intelligence: From Theories to Applications)
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