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 6706
Special Issue Editors
Interests: robotics; neural networks and machine learning; embedded system
Interests: application-driven accelerator design; hardware/software co-design
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
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Keywords
- embedded machine learning
- application-specific embedded system
- embedded software and architectures
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