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Optimizing the Convolution Operation to Accelerate Deep Neural Networks on FPGA
Optimizing the Convolution Operation to Accelerate Deep Neural Networks on FPGA
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Optimizing the Convolution Operation to Accelerate Deep Neural Networks on FPGA

Ma, Yufei ; Cao, Yu ; Vrudhula, Sarma ; Seo, Jae-sun

IEEE transactions on very large scale integration (VLSI) systems, 2018-07, Vol.26 (7), p.1354-1367 [Peer Reviewed Journal]

New York: IEEE

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2
A Heterogeneous Group CNN for Image Super-Resolution
A Heterogeneous Group CNN for Image Super-Resolution
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A Heterogeneous Group CNN for Image Super-Resolution

Tian, Chunwei ; Zhang, Yanning ; Zuo, Wangmeng ; Lin, Chia-Wen ; Zhang, David ; Yuan, Yixuan

IEEE transaction on neural networks and learning systems, 2024-05, Vol.35 (5), p.6507-6519

United States: IEEE

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3
Overview of the SpiNNaker System Architecture
Overview of the SpiNNaker System Architecture
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Overview of the SpiNNaker System Architecture

Furber, Steve B. ; Lester, David R. ; Plana, Luis A. ; Garside, Jim D. ; Painkras, Eustace ; Temple, Steve ; Brown, Andrew D.

IEEE transactions on computers, 2013-12, Vol.62 (12), p.2454-2467 [Peer Reviewed Journal]

New York: IEEE

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4
High Throughput/Gate AES Hardware Architectures Based on Datapath Compression
High Throughput/Gate AES Hardware Architectures Based on Datapath Compression
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High Throughput/Gate AES Hardware Architectures Based on Datapath Compression

Ueno, Rei ; Homma, Naofumi ; Morioka, Sumio ; Miura, Noriyuki ; Matsuda, Kohei ; Nagata, Makoto ; Bhasin, Shivam ; Mathieu, Yves ; Graba, Tarik ; Danger, Jean-Luc

IEEE transactions on computers, 2020-04, Vol.69 (4), p.534-548 [Peer Reviewed Journal]

IEEE

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5
Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models
Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models
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Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models

Minerva, Roberto ; Lee, Gyu Myoung ; Crespi, Noel

Proceedings of the IEEE, 2020-10, Vol.108 (10), p.1785-1824 [Peer Reviewed Journal]

New York: IEEE

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6
Fog-computing-based radio access networks: issues and challenges
Fog-computing-based radio access networks: issues and challenges
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Fog-computing-based radio access networks: issues and challenges

Peng, Mugen ; Yan, Shi ; Zhang, Kecheng ; Wang, Chonggang

IEEE network, 2016-07, Vol.30 (4), p.46-53 [Peer Reviewed Journal]

New York: IEEE

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7
5G wireless backhaul networks: challenges and research advances
5G wireless backhaul networks: challenges and research advances
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5G wireless backhaul networks: challenges and research advances

Xiaohu Ge ; Hui Cheng ; Guizani, Mohsen ; Tao Han

IEEE network, 2014-11, Vol.28 (6), p.6-11 [Peer Reviewed Journal]

IEEE

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8
Flexible and Efficient QoS Provisioning in AXI4-Based Network-on-Chip Architecture
Flexible and Efficient QoS Provisioning in AXI4-Based Network-on-Chip Architecture
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Flexible and Efficient QoS Provisioning in AXI4-Based Network-on-Chip Architecture

Wang, Boqian ; Lu, Zhonghai

IEEE transactions on computer-aided design of integrated circuits and systems, 2022-05, Vol.41 (5), p.1523-1536 [Peer Reviewed Journal]

New York: IEEE

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9
Cloud radio access network (C-RAN): a primer
Cloud radio access network (C-RAN): a primer
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Cloud radio access network (C-RAN): a primer

Wu, Jun ; Zhang, Zhifeng ; Hong, Yu ; Wen, Yonggang

IEEE network, 2015-01, Vol.29 (1), p.35-41 [Peer Reviewed Journal]

New York: IEEE

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10
System architecture and key technologies for 5G heterogeneous cloud radio access networks
System architecture and key technologies for 5G heterogeneous cloud radio access networks
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System architecture and key technologies for 5G heterogeneous cloud radio access networks

Peng, Mugen ; Li, Yong ; Zhao, Zhongyuan ; Wang, Chonggang

IEEE network, 2015-03, Vol.29 (2), p.6-14 [Peer Reviewed Journal]

New York: IEEE

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