PointAcc: Efficient Point Cloud Accelerator

@article{Lin2021PointAccEP,
  title={PointAcc: Efficient Point Cloud Accelerator},
  author={Yujun Lin and Zhekai Zhang and Haotian Tang and Hanrui Wang and Song Han},
  journal={MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture},
  year={2021}
}
  • Yujun Lin, Zhekai Zhang, Song Han
  • Published 14 October 2021
  • Computer Science
  • MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture
Deep learning on point clouds plays a vital role in a wide range of applications such as autonomous driving and AR/VR. These applications interact with people in real time on edge devices and thus require low latency and low energy. Compared to projecting the point cloud to 2D space, directly processing 3D point cloud yields higher accuracy and lower #MACs. However, the extremely sparse nature of point cloud poses challenges to hardware acceleration. For example, we need to explicitly determine… 
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