Potamoi: Accelerating Neural Rendering via a Unified Streaming Architecture
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929457582833664 |
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| author | Feng, Yu Lin, Weikai Liu, Zihan Leng, Jingwen Guo, Minyi Zhao, Han Hou, Xiaofeng Zhao, Jieru Zhu, Yuhao |
| author_facet | Feng, Yu Lin, Weikai Liu, Zihan Leng, Jingwen Guo, Minyi Zhao, Han Hou, Xiaofeng Zhao, Jieru Zhu, Yuhao |
| contents | Neural Radiance Field (NeRF) has emerged as a promising alternative for photorealistic rendering. Despite recent algorithmic advancements, achieving real-time performance on today's resource-constrained devices remains challenging. In this paper, we identify the primary bottlenecks in current NeRF algorithms and introduce a unified algorithm-architecture co-design, Potamoi, designed to accommodate various NeRF algorithms. Specifically, we introduce a runtime system featuring a plug-and-play algorithm, SpaRW, which significantly reduces the per-frame computational workload and alleviates compute inefficiencies. Furthermore, our unified streaming pipeline coupled with customized hardware support effectively tames both SRAM and DRAM inefficiencies by minimizing repetitive DRAM access and completely eliminating SRAM bank conflicts. When evaluated against a baseline utilizing a dedicated DNN accelerator, our framework demonstrates a speed-up and energy reduction of 53.1$\times$ and 67.7$\times$, respectively, all while maintaining high visual quality with less than a 1.0 dB reduction in peak signal-to-noise ratio. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_06608 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Potamoi: Accelerating Neural Rendering via a Unified Streaming Architecture Feng, Yu Lin, Weikai Liu, Zihan Leng, Jingwen Guo, Minyi Zhao, Han Hou, Xiaofeng Zhao, Jieru Zhu, Yuhao Hardware Architecture Graphics Neural Radiance Field (NeRF) has emerged as a promising alternative for photorealistic rendering. Despite recent algorithmic advancements, achieving real-time performance on today's resource-constrained devices remains challenging. In this paper, we identify the primary bottlenecks in current NeRF algorithms and introduce a unified algorithm-architecture co-design, Potamoi, designed to accommodate various NeRF algorithms. Specifically, we introduce a runtime system featuring a plug-and-play algorithm, SpaRW, which significantly reduces the per-frame computational workload and alleviates compute inefficiencies. Furthermore, our unified streaming pipeline coupled with customized hardware support effectively tames both SRAM and DRAM inefficiencies by minimizing repetitive DRAM access and completely eliminating SRAM bank conflicts. When evaluated against a baseline utilizing a dedicated DNN accelerator, our framework demonstrates a speed-up and energy reduction of 53.1$\times$ and 67.7$\times$, respectively, all while maintaining high visual quality with less than a 1.0 dB reduction in peak signal-to-noise ratio. |
| title | Potamoi: Accelerating Neural Rendering via a Unified Streaming Architecture |
| topic | Hardware Architecture Graphics |
| url | https://arxiv.org/abs/2408.06608 |