FLNA: Flexibly Accelerating Feature Learning Networks for Large-Scale Point Clouds With Efficient Dataflow Decoupling-Reference-Cited by-同舟云学术

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FLNA: Flexibly Accelerating Feature Learning Networks for Large-Scale Point Clouds With Efficient Dataflow Decoupling

Published:2024-04 Issue:4 Volume:32 Page:739-751
ISSN:1063-8210
Container-title:IEEE Transactions on Very Large Scale Integration (VLSI) Systems
language:
Short-container-title:IEEE Trans. VLSI Syst.

Author:

Lyu Dongxu¹^ORCID,Li Zhenyu¹^ORCID,Chen Yuzhou¹^ORCID,Wang Gang¹,He Weifeng¹^ORCID,Xu Ningyi²,He Guanghui³^ORCID

Affiliation:

1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China

2. Qing Yuan Research Institute, Shanghai Jiao Tong University, Shanghai, China

3. School of Electronic Information and Electrical Engineering and the MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/92/10477564/10416684.pdf?arnumber=10416684

Reference52 articles.

1. FLNA: An Energy-Efficient Point Cloud Feature Learning Accelerator with Dataflow Decoupling

2. Deep Learning for 3D Point Clouds: A Survey

3. VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

4. PointPillars: Fast Encoders for Object Detection From Point Clouds

5. SECOND: Sparsely Embedded Convolutional Detection

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