FPGA sharing in the cloud: a comprehensive analysis

Guo, Jinyang; Zhang, Lu; Hung, José Romero; Li, Chao; Zhao, Jieru; Guo, Minyi

doi:10.1007/s11704-022-2127-0

Cited by 6 publications

(1 citation statement)

References 77 publications

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“…To enhance the computational speed of embedded devices, we studied an acceleration platform that utilizes on-demand remote code execution [15]. The proposed platform optimizes the program execution time by partitioning code based on their requirements: Code requiring a fast response time is executed on the embedded devices, while code demanding extensive computation is executed on the server.…”

Section: Related Workmentioning

confidence: 99%

On-Cloud Linking Approach Using a Linkable Glue Layer for Metamorphic Edge Devices

Lee,

Park

2023

Electronics

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As sensors operating at the edge continue to evolve, the amount of data that edge devices need to process is increasing. Cloud computing methods have been proposed to process complex data on edge devices that are powered by limited resources. However, the existing cloud computing approach, which provides services from servers determined at the compile stage on the edge, is not suitable for the metamorphic edge device proposed in this paper. Therefore, we have realized the operation of metamorphic edge devices by changing the service that accelerates the application in real time according to the surrounding environmental conditions on the edge device. The on-cloud linking approach separates the code for communication from the edge and server into a linkable glue layer. The separated communication code in the linkable glue layer is reconfigured in real time according to the environment of the edge device. To verify the computational acceleration of cloud computing and the real-time service change of the metamorphic edge device, we operated services that perform matrix multiplication operations with one process, two processes, and four processes in parallel on the edge–cloud system based on the on-cloud linking approach. Through the experiments, it was confirmed that the on-cloud linking approach changes the service provided in real time according to changes in external environmental data without changing the code built into the edge. When a square matrix operation with 1000 rows was loaded onto the proposed platform, the size of the code embedded into the edge device decreased by 8.88% and the operation time decreased by 96.7%.

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Section: Related Workmentioning

confidence: 99%

On-Cloud Linking Approach Using a Linkable Glue Layer for Metamorphic Edge Devices

Lee,

Park

2023

Electronics

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A comprehensive survey of federated transfer learning: challenges, methods and applications

Guo,

Zhuang,

Zhang

et al. 2024

Front. Comput. Sci.

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Federated learning (FL) is a novel distributed machine learning paradigm that enables participants to collaboratively train a centralized model with privacy preservation by eliminating the requirement of data sharing. In practice, FL often involves multiple participants and requires the third party to aggregate global information to guide the update of the target participant. Therefore, many FL methods do not work well due to the training and test data of each participant may not be sampled from the same feature space and the same underlying distribution. Meanwhile, the differences in their local devices (system heterogeneity), the continuous influx of online data (incremental data), and labeled data scarcity may further influence the performance of these methods. To solve this problem, federated transfer learning (FTL), which integrates transfer learning (TL) into FL, has attracted the attention of numerous researchers. However, since FL enables a continuous share of knowledge among participants with each communication round while not allowing local data to be accessed by other participants, FTL faces many unique challenges that are not present in TL. In this survey, we focus on categorizing and reviewing the current progress on federated transfer learning, and outlining corresponding solutions and applications. Furthermore, the common setting of FTL scenarios, available datasets, and significant related research are summarized in this survey.

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