StreamGrid: Streaming Point Cloud Analytics via Compulsory Splitting and Deterministic Termination
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913918479237120 |
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| author | Feng, Yu Liu, Zheng Lin, Weikai Liu, Zihan Leng, Jingwen Guo, Minyi He, Zhezhi Zhao, Jieru Zhu, Yuhao |
| author_facet | Feng, Yu Liu, Zheng Lin, Weikai Liu, Zihan Leng, Jingwen Guo, Minyi He, Zhezhi Zhao, Jieru Zhu, Yuhao |
| contents | Point clouds are increasingly important in intelligent applications, but frequent off-chip memory traffic in accelerators causes pipeline stalls and leads to high energy consumption. While conventional line buffer techniques can eliminate off-chip traffic, they cannot be directly applied to point clouds due to their inherent computation patterns. To address this, we introduce two techniques: compulsory splitting and deterministic termination, enabling fully-streaming processing. We further propose StreamGrid, a framework that integrates these techniques and automatically optimizes on-chip buffer sizes. Our evaluation shows StreamGrid reduces on-chip memory by 61.3\% and energy consumption by 40.5\% with marginal accuracy loss compared to the baselines without our techniques. Additionally, we achieve 10.0$\times$ speedup and 3.9$\times$ energy efficiency over state-of-the-art accelerators. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_05197 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | StreamGrid: Streaming Point Cloud Analytics via Compulsory Splitting and Deterministic Termination Feng, Yu Liu, Zheng Lin, Weikai Liu, Zihan Leng, Jingwen Guo, Minyi He, Zhezhi Zhao, Jieru Zhu, Yuhao Hardware Architecture Point clouds are increasingly important in intelligent applications, but frequent off-chip memory traffic in accelerators causes pipeline stalls and leads to high energy consumption. While conventional line buffer techniques can eliminate off-chip traffic, they cannot be directly applied to point clouds due to their inherent computation patterns. To address this, we introduce two techniques: compulsory splitting and deterministic termination, enabling fully-streaming processing. We further propose StreamGrid, a framework that integrates these techniques and automatically optimizes on-chip buffer sizes. Our evaluation shows StreamGrid reduces on-chip memory by 61.3\% and energy consumption by 40.5\% with marginal accuracy loss compared to the baselines without our techniques. Additionally, we achieve 10.0$\times$ speedup and 3.9$\times$ energy efficiency over state-of-the-art accelerators. |
| title | StreamGrid: Streaming Point Cloud Analytics via Compulsory Splitting and Deterministic Termination |
| topic | Hardware Architecture |
| url | https://arxiv.org/abs/2503.05197 |