Zobrazeno 1 - 10
of 93
pro vyhledávání: '"Zhu, Jingyang"'
Space-ground integrated networks hold great promise for providing global connectivity, particularly in remote areas where large amounts of valuable data are generated by Internet of Things (IoT) devices, but lacking terrestrial communication infrastr
Externí odkaz:
http://arxiv.org/abs/2408.14116
The proliferation of low-earth-orbit (LEO) satellite networks leads to the generation of vast volumes of remote sensing data which is traditionally transferred to the ground server for centralized processing, raising privacy and bandwidth concerns. F
Externí odkaz:
http://arxiv.org/abs/2404.01875
Federated learning (FL), as an emerging distributed machine learning paradigm, allows a mass of edge devices to collaboratively train a global model while preserving privacy. In this tutorial, we focus on FL via over-the-air computation (AirComp), wh
Externí odkaz:
http://arxiv.org/abs/2310.10089
Publikováno v:
In Journal of Solid State Chemistry August 2024 336
Autor:
Meng, Runze, Zhong, Xiuli, Gong, Yue, Shi, Yulong, Li, Jiayu, Wu, Zhiyun, Duan, Qionglu, Zhang, Xintong, Mei, Yuheng, Zhu, Jingyang, Peng, Zonggen, Li, Yinghong, Song, Danqing
Publikováno v:
In Bioorganic Chemistry June 2024 147
Autor:
Zhu, Jingyang, Li, Shurong
Publikováno v:
In Journal of the Franklin Institute March 2024 361(4)
Publikováno v:
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 42, no. 2, pp. 644-657, Feb. 2023
The unstructured sparsity after pruning poses a challenge to the efficient implementation of deep learning models in existing regular architectures like systolic arrays. On the other hand, coarse-grained structured pruning is suitable for implementat
Externí odkaz:
http://arxiv.org/abs/2104.01303
In this paper, we consider federated learning (FL) over a noisy fading multiple access channel (MAC), where an edge server aggregates the local models transmitted by multiple end devices through over-the-air computation (AirComp). To realize efficien
Externí odkaz:
http://arxiv.org/abs/2011.06658
Akademický článek
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Recently Resistive-RAM (RRAM) crossbar has been used in the design of the accelerator of convolutional neural networks (CNNs) to solve the memory wall issue. However, the intensive multiply-accumulate computations (MACs) executed at the crossbars dur
Externí odkaz:
http://arxiv.org/abs/1906.03180