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Electronics
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Towards Efficient and Reliable AI at the Edge
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Towards Efficient and Reliable AI at the Edge

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 August 2024 | Viewed by 6294

Special Issue Editor

Dr. Palden Lama

E-Mail Website
Guest Editor
Department of Computer Science, the University of Texas at San Antonio, San Antonio, TX 78249, USA
Interests: cloud computing and data centers; edge computing; big data; cyber security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The deployment of AI models on edge devices has gained significant traction due to its potential to enable real-time decision making and alleviate the dependency on cloud-based services. However, this burgeoning field faces numerous challenges such as limited computational resources, power constraints, and unreliable network connectivity. Moreover, with critical infrastructure such as healthcare, robotic manufacturing, utilities, and telecommunications embracing AI-capable devices, ensuring reliability and security also becomes crucial. This Special Issue invites novel contributions that address these challenges and propose innovative solutions to enhance the performance, reliability, and security of AI models on edge devices.  

Topics of interest include, but are not limited to, the following: 

  • Efficient AI model design for edge devices: 
    • Model compression and quantization;
    • Lightweight network architecture;
    • Federated learning and collaborative approaches; 
  • Optimization of edge device resources: 
    • Energy efficient computing and communication; 
    • Dynamic resource management; 
    • Edge-cloud collaboration; 
  • Reliable operation of edge AI: 
    • Robustness against hardware failures; 
    • Fault tolerance and resilience; 
  • Security and privacy in edge AI:
    • Secure model deployment; 
    • Privacy preservation techniques; 
    • Defense against adversarial attacks; 
  • Evaluation and benchmarking of edge AI: 
    • Real-world case studies and evaluation;
    • Comparative analysis of edge AI frameworks;  
    • Evaluation frameworks for edge AI systems.

Dr. Palden Lama
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. 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

  • edge AI
  • performance
  • reliability
  • security
  • privacy preservation
  • energy efficiency
  • model compression
  • lightweight architectures
  • federated learning
  • resource optimization
  • edge–cloud collaboration

Published Papers (6 papers)

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Research

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31 pages, 1698 KiB  
Article
Flexible Deployment of Machine Learning Inference Pipelines in the Cloud–Edge–IoT Continuum
by Karolina Bogacka, Piotr Sowiński, Anastasiya Danilenka, Francisco Mahedero Biot, Katarzyna Wasielewska-Michniewska, Maria Ganzha, Marcin Paprzycki and Carlos E. Palau
Electronics 2024, 13(10), 1888; https://doi.org/10.3390/electronics13101888 (registering DOI) - 11 May 2024
Viewed by 81
Abstract
Currently, deploying machine learning workloads in the Cloud–Edge–IoT continuum is challenging due to the wide variety of available hardware platforms, stringent performance requirements, and the heterogeneity of the workloads themselves. To alleviate this, a novel, flexible approach for machine learning inference is introduced, [...] Read more.
Currently, deploying machine learning workloads in the Cloud–Edge–IoT continuum is challenging due to the wide variety of available hardware platforms, stringent performance requirements, and the heterogeneity of the workloads themselves. To alleviate this, a novel, flexible approach for machine learning inference is introduced, which is suitable for deployment in diverse environments—including edge devices. The proposed solution has a modular design and is compatible with a wide range of user-defined machine learning pipelines. To improve energy efficiency and scalability, a high-performance communication protocol for inference is propounded, along with a scale-out mechanism based on a load balancer. The inference service plugs into the ASSIST-IoT reference architecture, thus taking advantage of its other components. The solution was evaluated in two scenarios closely emulating real-life use cases, with demanding workloads and requirements constituting several different deployment scenarios. The results from the evaluation show that the proposed software meets the high throughput and low latency of inference requirements of the use cases while effectively adapting to the available hardware. The code and documentation, in addition to the data used in the evaluation, were open-sourced to foster adoption of the solution. Full article
(This article belongs to the Special Issue Towards Efficient and Reliable AI at the Edge)
18 pages, 7538 KiB  
Article
Semantic Segmentation Network Slimming and Edge Deployment for Real-Time Forest Fire or Flood Monitoring Systems Using Unmanned Aerial Vehicles
by Youn Joo Lee, Ho Gi Jung and Jae Kyu Suhr
Electronics 2023, 12(23), 4795; https://doi.org/10.3390/electronics12234795 - 27 Nov 2023
Viewed by 847
Abstract
In recent years, there has been a significant increase in the demand for unmanned aerial vehicle (UAV)-based monitoring systems to ensure proper emergency response during natural disasters such as wildfires, hurricanes, floods, and earthquakes. This paper proposes a real-time UAV monitoring system for [...] Read more.
In recent years, there has been a significant increase in the demand for unmanned aerial vehicle (UAV)-based monitoring systems to ensure proper emergency response during natural disasters such as wildfires, hurricanes, floods, and earthquakes. This paper proposes a real-time UAV monitoring system for responding to forest fires or floods. The proposed system consists of a hardware part and a software part. The hardware configuration is an embedded camera board mounted on the UAV, a Qualcomm QCS610 SoC with cores suitable for running deep learning-based algorithms. The software configuration is a deep learning-based semantic segmentation model for detecting fires or floods. To execute the model in real time on edge devices with limited resources, we used a network slimming technique which generates a lightweight model with reduced model size, number of parameters, and computational complexity. The performance of the proposed system was evaluated on the FLAME dataset consisting of forest fire images and the FloodNet dataset consisting of flood images. The experimental results showed that the mIoU of slimmed DeepLabV3+ for FLAME is 88.29%, and the inference speed is 10.92 fps. For FloodNet, the mIoU of the slimmed DeepLabV3+ is 94.15%, and the inference speed is 13.26 fps. These experimental results confirm that the proposed system is appropriate for accurate, low-power, real-time monitoring of forest fires and floods using UAVs. Full article
(This article belongs to the Special Issue Towards Efficient and Reliable AI at the Edge)
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19 pages, 957 KiB  
Article
Puppis: Hardware Accelerator of Single-Shot Multibox Detectors for Edge-Based Applications
by Vladimir Vrbaski, Slobodan Josic, Vuk Vranjkovic, Predrag Teodorovic and Rastislav Struharik
Electronics 2023, 12(22), 4557; https://doi.org/10.3390/electronics12224557 - 7 Nov 2023
Viewed by 703
Abstract
Object detection is a popular image-processing technique, widely used in numerous applications for detecting and locating objects in images or videos. While being one of the fastest algorithms for object detection, Single-shot Multibox Detection (SSD) networks are also computationally very demanding, which limits [...] Read more.
Object detection is a popular image-processing technique, widely used in numerous applications for detecting and locating objects in images or videos. While being one of the fastest algorithms for object detection, Single-shot Multibox Detection (SSD) networks are also computationally very demanding, which limits their usage in real-time edge applications. Even though the SSD post-processing algorithm is not the most-complex segment of the overall SSD object-detection network, it is still computationally demanding and can become a bottleneck with respect to processing latency and power consumption, especially in edge applications with limited resources. When using hardware accelerators to accelerate backbone CNN processing, the SSD post-processing step implemented in software can become the bottleneck for high-end applications where high frame rates are required, as this paper shows. To overcome this problem, we propose Puppis, an architecture for the hardware acceleration of the SSD post-processing algorithm. As the experiments showed, our solution led to an average SSD post-processing speedup of 33.34-times when compared with a software implementation. Furthermore, the execution of the complete SSD network was on average 36.45-times faster than the software implementation when the proposed Puppis SSD hardware accelerator was used together with some existing CNN accelerators. Full article
(This article belongs to the Special Issue Towards Efficient and Reliable AI at the Edge)
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20 pages, 5390 KiB  
Article
Cloud–Edge Collaborative Inference with Network Pruning
by Mingran Li, Xuejun Zhang, Jiasheng Guo and Feng Li
Electronics 2023, 12(17), 3598; https://doi.org/10.3390/electronics12173598 - 25 Aug 2023
Viewed by 1236
Abstract
With the increase in model parameters, deep neural networks (DNNs) have achieved remarkable performance in computer vision, but larger DNNs create a bottleneck for deploying DNNs on resource-constrained edge devices. The cloud–edge collaborative inference based on network pruning provides a solution for the [...] Read more.
With the increase in model parameters, deep neural networks (DNNs) have achieved remarkable performance in computer vision, but larger DNNs create a bottleneck for deploying DNNs on resource-constrained edge devices. The cloud–edge collaborative inference based on network pruning provides a solution for the deployment of DNNs on edge devices. However, the pruning methods adopted by existing frameworks are locally effective, and the compressed models are over-sparse. In this paper, we design a cloud–edge collaborative inference framework based on network pruning to make full use of the limited computing resources on edge devices. In our framework, we propose a sparsity-aware feature bias minimization pruning method to reduce the feature bias that happens during network pruning and prevent the pruned model from being over-sparse. To further reduce the inference latency, we consider the difference in computing resources between edge devices and the cloud, then design a task-oriented asymmetric feature coding to reduce the communication overhead of transmitting intermediate data. With comprehensive experiments, our framework can reduce end-to-end latency by 82% to 84% with less than 1% accuracy loss, compared to the cloud–edge collaborative inference framework with traditional methods, and our framework has the lowest end-to-end latency and accuracy loss compared to other frameworks. Full article
(This article belongs to the Special Issue Towards Efficient and Reliable AI at the Edge)
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Review

Jump to: Research

23 pages, 680 KiB  
Review
Containerization in Edge Intelligence: A Review
by Lubomir Urblik, Erik Kajati, Peter Papcun and Iveta Zolotová
Electronics 2024, 13(7), 1335; https://doi.org/10.3390/electronics13071335 - 2 Apr 2024
Viewed by 685
Abstract
The onset of cloud computing brought with it an adoption of containerization—a lightweight form of virtualization, which provides an easy way of developing and deploying solutions across multiple environments and platforms. This paper describes the current use of containers and complementary technologies in [...] Read more.
The onset of cloud computing brought with it an adoption of containerization—a lightweight form of virtualization, which provides an easy way of developing and deploying solutions across multiple environments and platforms. This paper describes the current use of containers and complementary technologies in software development and the benefits it brings. Certain applications run into obstacles when deployed on the cloud due to the latency it introduces or the amount of data that needs to be processed. These issues are addressed by edge intelligence. This paper describes edge intelligence, the deployment of artificial intelligence close to the data source, the opportunities it brings, along with some examples of practical applications. We also discuss some of the challenges in the development and deployment of edge intelligence solutions and the possible benefits of applying containerization in edge intelligence. Full article
(This article belongs to the Special Issue Towards Efficient and Reliable AI at the Edge)
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26 pages, 352 KiB  
Review
Combining Machine Learning and Edge Computing: Opportunities, Challenges, Platforms, Frameworks, and Use Cases
by Piotr Grzesik and Dariusz Mrozek
Electronics 2024, 13(3), 640; https://doi.org/10.3390/electronics13030640 - 3 Feb 2024
Viewed by 2149
Abstract
In recent years, we have been observing the rapid growth and adoption of IoT-based systems, enhancing multiple areas of our lives. Concurrently, the utilization of machine learning techniques has surged, often for similar use cases as those seen in IoT systems. In this [...] Read more.
In recent years, we have been observing the rapid growth and adoption of IoT-based systems, enhancing multiple areas of our lives. Concurrently, the utilization of machine learning techniques has surged, often for similar use cases as those seen in IoT systems. In this survey, we aim to focus on the combination of machine learning and the edge computing paradigm. The presented research commences with the topic of edge computing, its benefits, such as reduced data transmission, improved scalability, and reduced latency, as well as the challenges associated with this computing paradigm, like energy consumption, constrained devices, security, and device fleet management. It then presents the motivations behind the combination of machine learning and edge computing, such as the availability of more powerful edge devices, improving data privacy, reducing latency, or lowering reliance on centralized services. Then, it describes several edge computing platforms, with a focus on their capability to enable edge intelligence workflows. It also reviews the currently available edge intelligence frameworks and libraries, such as TensorFlow Lite or PyTorch Mobile. Afterward, the paper focuses on the existing use cases for edge intelligence in areas like industrial applications, healthcare applications, smart cities, environmental monitoring, or autonomous vehicles. Full article
(This article belongs to the Special Issue Towards Efficient and Reliable AI at the Edge)
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