Special Issue "Recent Advances in Microelectronics Devices and Integrated Circuit"

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

Deadline for manuscript submissions: 15 January 2023 | Viewed by 1577

Special Issue Editors

Dr. Teo Tee Hui
E-Mail Website
Guest Editor
Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore
Interests: low-power and low-voltage design for sensor interface; mixed-signal wireless; AI integrated circuit
Dr. Akram Ben Ahmed
E-Mail Website
Guest Editor
Digital Architecture Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan
Interests: low-power embedded systems; on-chip interconnect; reliable SoC
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Special Issue Information

Dear Colleagues,

Computationally intelligent devices are becoming the mainstream due to the emerging technology around Artificial Intelligence, which is particularly deep-learning-driven. AI is everywhere now and accelerating all the technologies in microelectronics and related integrated circuits, such as direct memory, processing units, display/motor drivers, sensor interfaces, parallel computing, etc. Cryptography is also triggering the fast growth of quantum computing devices and circuits. Advanced microelectronics and integrated circuits have enabled the long-awaited practical implementation of Artificial Intelligence in a reasonable computation speed.

Accordingly, this Special Issue seeks to showcase research papers and review articles that focus on novel methodological developments in the field of advanced microelectronics and the design of related integrated circuits. Work that explores how technologies can be incorporated with AI methods to identify novel design directions and improve devices, circuits, system performance, as well as processes is sought after. Additionally, research which identifies and evaluates the limitations of applying AI to certain problems and specific domains, in advanced microelectronics and integrated circuits, is also welcome.

This Special Issue is primarily meant to serve as a collection of extended versions of selected papers presented at the IEEE 15th International Symposium on Embedded Multicore/Manycore Systems-on-Chip (MCSoC-2022) https://www.mcsoc-forum.org/. Selected papers from MCSoC 2022 and all external contributions in this field are welcome in this Special Issue.

Dr. Teo Tee Hui
Dr. Akram Ben Ahmed
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 2000 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

  • artificial intelligence
  • deep learning
  • integrated circuits
  • microelectronics
  • quantum computing

Published Papers (1 paper)

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Research

Article
Implementation of Binarized Neural Networks in All-Programmable System-on-Chip Platforms
Electronics 2022, 11(4), 663; https://doi.org/10.3390/electronics11040663 - 21 Feb 2022
Cited by 1 | Viewed by 710
Abstract
The Binarized Neural Network (BNN) is a Convolutional Neural Network (CNN) consisting of binary weights and activation rather than real-value weights. Smaller models are used, allowing for inference effectively on mobile or embedded devices with limited power and computing capabilities. Nevertheless, binarization results [...] Read more.
The Binarized Neural Network (BNN) is a Convolutional Neural Network (CNN) consisting of binary weights and activation rather than real-value weights. Smaller models are used, allowing for inference effectively on mobile or embedded devices with limited power and computing capabilities. Nevertheless, binarization results in lower-entropy feature maps and gradient vanishing, which leads to a loss in accuracy compared to real-value networks. Previous research has addressed these issues with various approaches. However, those approaches significantly increase the algorithm’s time and space complexity, which puts a heavy burden on those embedded devices. Therefore, a novel approach for BNN implementation on embedded systems with multi-scale BNN topology is proposed in this paper, from two optimization perspectives: hardware structure and BNN topology, that retains more low-level features throughout the feed-forward process with few operations. Experiments on the CIFAR-10 dataset indicate that the proposed method outperforms a number of current BNN designs in terms of efficiency and accuracy. Additionally, the proposed BNN was implemented on the All Programmable System on Chip (APSoC) with 4.4 W power consumption using the hardware accelerator. Full article
(This article belongs to the Special Issue Recent Advances in Microelectronics Devices and Integrated Circuit)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Authors: Dr. Kiat Seng Yeo, Dr. Bharatha Kumar, Dr. Bithin Alangot, Dr. Dipu Bhaskar

Affiliation: Engineering Product Development (EPD), Singapore University of Technology and Design, Singapore 487372, Singapore

Title: limitations of applying artificial intelligence to certain problems and specific domains

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