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Electronics
Special Issues
Emerging and New Technologies in Embedded Systems
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Emerging and New Technologies in Embedded Systems

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 June 2023) | Viewed by 5209

Special Issue Editors

Dr. Gang Chen

E-Mail Website
Guest Editor
Department of Computer Science, Sun Yat-sen University, Guangzhou 510275, China
Interests: robotics; neural networks and machine learning; embedded system
Dr. Letian Huang

E-Mail Website
Guest Editor
School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
Interests: application-driven accelerator design; hardware/software co-design
Dr. Di Liu

E-Mail Website
Guest Editor
School of Software, Yunnan University, Yunnan 650106, China
Interests: edge intelligence; embedded systems

Special Issue Information

Dear Colleagues,

This Special Issue (SI) invites papers on research achievements in emerging and new technologies in embedded systems. Embedded systems have matured as a discriminating technology for a wide variety of applications in our daily life. However, this immense impact is unfortunately confined by the power and cost constraints of hardware devices. A growing gap has been observed between computing demands of modern applications and the availability of hardware resources in embedded systems, thereby raising many unique research challenges and research questions. Recent technologies have evolved tremendously to approach a wide range of applications and implementations that push such limits in the performances of embedded computing. Many novel hardware architectures are deployed for improving the performance of embedded systems, while emerging applications, e.g., deep learning, are implemented on embedded systems to pave the way of ubiquitous AI. In this SI, we look forward to the latest, original research work that suggests new architecture and practical solutions for various applications of embedded systems. Authors are encouraged to submit contributions in any of the following or related areas:

  • Embedded machine learning;
  • Safety-critical embedded systems;
  • Hardware/software co-optimization;
  • Reconfigurable and self-adaptive architectures;
  • Application-specific processors and accelerators;
  • Energy-aware system design and methodologies;
  • Embedded operating systems and middleware;
  • Industrial practices and case studies;
  • Internet of Things.

Dr. Gang Chen
Dr. Letian Huang
Dr. Di Liu
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.

Keywords

  • embedded machine learning
  • application-specific embedded system
  • embedded software and architectures

Published Papers (2 papers)

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Research

19 pages, 4380 KiB  
Article
A Hardware Non-Invasive Mapping Method for Condition Bits in Binary Translation
by Chunqiang Li, Zhiwei Liu, Yunhai Shang, Lenian He and Xiaolang Yan
Electronics 2023, 12(14), 3014; https://doi.org/10.3390/electronics12143014 - 9 Jul 2023
Cited by 1 | Viewed by 1163
Abstract
Binary translation, as an important bridge for application compatibility between different instruction set architectures (ISAs), has attracted much attention in the industry. However, due to hardware resource limitations of the target ISA, the translation efficiency and the practicability are poor. Recently, Apple has [...] Read more.
Binary translation, as an important bridge for application compatibility between different instruction set architectures (ISAs), has attracted much attention in the industry. However, due to hardware resource limitations of the target ISA, the translation efficiency and the practicability are poor. Recently, Apple has made it possible to run x86 programs on ARM through a translation technology called Rosetta based on software-hardware collaboration. In this paper, we proposed a hardware non-invasive mapping method for condition bits (HNIMCB) in binary translation, which innovatively implements the setting and referencing operations of the condition bits without changing the original instruction encoding and function of the target processor. This method is applicable for binary translation from source architectures with condition bit operations to target architectures without condition bit operations. It eliminates the difference of conditional bit resources between the source and target ISAs, reduces the computational instructions and memory access operations after translation from the source to the target ISA, and dramatically improves the translation efficiency. We conducted this experiment on a functional simulation level using the QEMU binary translator from ARM to RISC-V. A series of benchmark tests revealed that the total number of instructions decreased by 41%, while the number of memory access instructions decreased by 37% after the translation applying with the HNIMCB. Full article
(This article belongs to the Special Issue Emerging and New Technologies in Embedded Systems)
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14 pages, 579 KiB  
Article
KHV: KVM-Based Heterogeneous Virtualization
by Chunqiang Li, Ren Guo, Xianting Tian and Huibin Wang
Electronics 2022, 11(16), 2631; https://doi.org/10.3390/electronics11162631 - 22 Aug 2022
Cited by 2 | Viewed by 3047
Abstract
A KVM (Kernel-based Virtual Machine) is subject to the complexity of the Linux kernel and the difficulty and cost of safety certification; thus, it is not popularized in embedded high-reliability scenarios. This paper proposes a KVM-based Heterogeneous Virtualization (KHV), which is independent of [...] Read more.
A KVM (Kernel-based Virtual Machine) is subject to the complexity of the Linux kernel and the difficulty and cost of safety certification; thus, it is not popularized in embedded high-reliability scenarios. This paper proposes a KVM-based Heterogeneous Virtualization (KHV), which is independent of hardware virtualization (KVM mandatory virtualization), follows the principle of static partitioning, localizes the hypervisor, and inherits the KVM software ecosystem. KHV balances the demands of static partitioning and flexible sharing in the embedded system. The paper implemented KHV on the RISC-V Xuantie C910 CPU-based SoC and conducted a performance comparison with KVM. The experiment shows that KHV is 50% smaller than KVM in terms of fluctuation, and KHV makes the guest OS have the same performance as the bare-metal OS in scheduler benchmarks, whereas KVM dropped an average of 28%. Full article
(This article belongs to the Special Issue Emerging and New Technologies in Embedded Systems)
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