Smart Network Maintenance in an Edge Cloud Computing Environment: An Adaptive Model Compression Algorithm Based on Model Pruning and Model Clustering

Rui, Lanlan; Yang, Siqi; Chen, Shiyou; Yang, Yang; Gao, Zhipeng

doi:10.1109/tnsm.2022.3202796

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…For example, large, middle-size, and lightweight models can be placed in cloud servers, edge serves, and user devices, respectively. Different resolutions of the models can be achieved through knowledge distillation [126,40] and model parameter pruning [104,55]. Grouping users with the same computation facility can further reduce the computation.…”

Section: B Artificial Intelligence Of Thingsmentioning

confidence: 99%

An Overview on Generative AI at Scale With Edge–Cloud Computing

Wang,

Xue,

Wei

et al. 2023

IEEE Open J. Commun. Soc.

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As a specific category of artificial intelligence (AI), generative artificial intelligence (GenAI) generates new content that resembles what humans create. The rapid development of GenAI systems has created a huge amount of new data on the Internet, posing new challenges to current computing and communication frameworks. Currently, GenAI services rely on the traditional cloud computing framework due to the need for large computation resources. However, such services will encounter high latency because of data transmission and a high volume of user requests. On the other hand, edge-cloud computing can provide adequate computation power and low latency at the same time through the collaboration between edges and the cloud. Thus, it is attractive to build GenAI systems at scale by leveraging the edge-cloud computing paradigm. In this overview paper, we review recent developments in GenAI and edge-cloud computing, respectively. Then, we use two exemplary GenAI applications to discuss technical challenges in scaling up their solutions using edge-cloud collaborative systems. Finally, we list design considerations for training and deploying GenAI systems at scale and point out future research directions.

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Section: B Artificial Intelligence Of Thingsmentioning

confidence: 99%

An Overview on Generative AI at Scale With Edge–Cloud Computing

Wang,

Xue,

Wei

et al. 2023

IEEE Open J. Commun. Soc.

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“…The pruning methods can be either weight pruning, channel pruning, or hybrid pruning. The weight pruning approach removes the redundant weight and only keeps weights that contribute to the result [188][189][190][191][192], whereas in channel pruning, unnecessary channels are removed from the feature images [193][194][195][196][197][198][199][200]. A pre-trained teacher network architecture is given, and the objective of hybrid pruning [99,201,202] is to seek and identify the shortest network model from that architecture while preserving the best level of accuracy.…”

Section: Pruning In Wall Segmentation Of Ivus Scanmentioning

confidence: 99%

Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look

Kumari,

Kumar,

Kumar K

et al. 2023

JCDD

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Background and Motivation: Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias. Methods: Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework. Findings and Conclusions: UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach.

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