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Special Issue "Near-Sensor DNN Computing"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 30 April 2022.
Please contact the Guest Editor or the Section Managing Editor at ([email protected]) for any queries.

Special Issue Editors

Dr. Jridi Maher
E-Mail
Guest Editor
[email protected], ISEN Yncrea Ouest, 33 quater chemin du champ de Manœuvre, 44470 Carquefou, France
Interests: Digital VLSI design; Smart Vision systems; FPGA design; Image and video coding; Reconfigurable architectures
Dr. Thibault Napoléon
E-Mail
Guest Editor
[email protected], ISEN Yncrea Ouest, 33 quater chemin du champ de Manœuvre, 44470 Carquefou, France
Interests: computer vision
Dr. Ayoub Karine
E-Mail Website
Guest Editor
[email protected], ISEN Yncrea Ouest, 33 quater chemin du champ de Manœuvre, 44470 Carquefou, France
Interests: Machine learning; Deep learning; Computer vision

Special Issue Information

Dear Colleagues,

In recent years, deep neural networks (DNNs) have achieved overwhelming success in different artificial intelligence applications. This great success is mainly due to the availability of the GPU and TPU clusters that can train very deep models with thousands of layers and millions/billions of parameters on large-scale datasets. However, such cumbersome DNNs require heavy computation resources, which makes their deployment on devices with limited computational capacity and memory (embedded devices, mobile phones, etc.) very difficult. To overcome this limitation, algorithmic, architectural, and technological efforts could be made. From the algorithmic point of view, DNN compression techniques seem to be an attractive solution. Moreover, some innovative architectures and design flows have been attempted for deployment to reach the compromise between precision and energy efficiency. To summarize, the main challenge is to propose heavy architectures or optimized algorithms that achieve approximately the same performance when compared to the original versions.

This Special Issue aims to cover the new developments and recent advances in the compression of the deep neural networks for real-time applications. The topic includes but is not limited to the following:

  • Cloud/FoG/Edge DNN challenges;
  • Knowledge distillation;
  • Parameters pruning and quantization;
  • Design flow and low power systems;
  • Low-rank factorization;
  • Transferred compact convolutional filters;
  • Hardware accelerators.

Dr. Jridi Maher
Dr. Thibault Napoléon
Dr. Ayoub Karine
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 papers will be 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. Sensors 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 2200 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

  • deep neural networks
  • knowledge distillation
  • pruning/quantization model compression
  • computer visions
  • internet of things

Published Papers

This special issue is now open for submission.
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