An Energy-Efficient 3D Point Cloud Neural Network Accelerator With Efficient Filter Pruning, MLP Fusion, and Dual-Stream Sampling-Reference-Cited by-同舟云学术

An Energy-Efficient 3D Point Cloud Neural Network Accelerator With Efficient Filter Pruning, MLP Fusion, and Dual-Stream Sampling

Published:2023-10-28 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)
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Author:

Zhou Changchun¹,Fu Yuzhe¹,Liu Min¹,Qiu Siyuan¹,Li Ge¹,He Yifan²,Jiao Hailong¹

Affiliation:

1. School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University,Shenzhen,China

2. Reconova Technologies Co., Ltd.,Xiamen,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10323590/10323543/10323704.pdf?arnumber=10323704

Reference26 articles.

1. PointWeb: Enhancing Local Neighborhood Features for Point Cloud Processing

2. PointNeXt: Revisiting PointNet++ with improved training and scaling strategies;qian;Proc of International Conference on Neural Information Processing,2022

3. A 28nm 2D/3D Unified Sparse Convolution Accelerator with Block-Wise Neighbor Searcher for Large-Scaled Voxel-Based Point Cloud Network

4. A Sparse Convolution Neural Network Accelerator for 3D/4D Point-Cloud Image Recognition on Low Power Mobile Device with Hopping-Index Rule Book for Efficient Coordinate Management

5. Rethinking network design and local geometry in point cloud: A simple residual MLP framework;ma;Proc of the International Conference on Learning Representations,2022

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