Collaborative Research: SHF: Small: Enabling Efficient 3D Perception: An Architecture-Algorithm Co-Design Approach

协作研究：SHF：小型：实现高效的 3D 感知：架构-算法协同设计方法

• 批准号: 2334624
• 负责人: Guoyu Lu
• 金额: $ 21.49万
• 依托单位: University of Georgia Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2334624&HistoricalAwards=false
• 关键词: Collaborative; Research; SHF; Small; Enabling

This research project designs software-hardware collaborative mechanisms to boost the execution efficiency of 3D perception (point cloud) algorithms by an order of magnitude. Achieving this goal requires designing fundamentally new algorithmic primitives, offline and run-time systems, and hardware architectures that tame the irregular computation and memory patterns in 3D perception algorithms. This research unlocks next-generation software innovation in emerging domains driven by 3D perception, such as autonomous driving, Augmented/Virtual Reality, and precision agriculture. The research agenda is complemented by an educational/outreach agenda. The PIs are (1) offering summer “introduction to computing” courses to high school students from the Rochester Central School District, (2) engaging students in RIT’s National Technical Institute for the Deaf program through experiencing 3D sensing and research activities, (3) introducing new courses/modules in both UR and RIT on 3D perception, both on algorithms and hardware systems, and (4) offering undergraduate students inclusive opportunities for hands-on experience in emerging application domains and hardware acceleration.This research project addresses the fundamental mismatch between the irregularities in point-cloud algorithms and today’s hardware architectures, which are primarily optimized for 2D image- and video-processing algorithms that are regular stencil pipelines operating on structured data. The key intellectual merit is the pursuit of new algorithms and system architectures that reduce/eliminate irregular computation and memory accesses in 3D perception. The technical contribution is three-fold: 1) efficient, yet generally applicable, hardware building blocks required to accelerate point cloud algorithms, 2) run-time systems that dynamically adapt to operating constraints (e.g., hardware resources, performance, energy) in an application-aware and data-aware manner, and 3) a new class of efficient-by-construction 3D perception algorithms that leverage the small data volume in single-beam point clouds. The algorithm-hardware co-designed system not only accelerates current 3D perception algorithms, but also provides a computing substrate so that 3D perception can be pervasively used as a building block in future application domains.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该研究项目设计了软件硬件协作机制，以提高3D感知（点云）算法的执行效率。实现此目标需要设计新的算法原始算法，离线和运行时系统，以及在3D感知算法中驯服不规则计算和内存模式的硬件体系结构。这项研究通过3D感知来解锁新兴域中的下一代软件创新，例如自动驾驶，增强/虚拟现实和精确农业。研究议程由教育/外展议程完成。 PI（1）为来自罗切斯特中央学区的高中学生提供夏季“计算概论”课程，（2）通过体验3D感应和研究活动，让学生参与RIT的国家聋人技术研究所，（3）介绍您的新课程/模块，以进行3D感知和RIT，并在altgorment和RIT上进行杂物，以及4D的范围（4D），以及4D的范围（4D）。该研究项目在新兴的应用领域和硬件加速度方面进行动手实践经验。解决了Point-Cloud算法中的不规则性与当今的硬件体系结构之间的基本不匹配，这些算法主要针对2D图像和视频处理算法进行了优化，这些算法是在结构数据上运行的常规模板管道。关键的智力优点是追求新算法和系统体系结构，这些算法和系统体系结构减少/消除了3D感知中不规则的计算和内存访问。技术贡献是三个方面的：1）加速点云算法所需的高效，但通常适用的硬件构建块，2）运行时系统，这些系统以动态适应运营约束（例如硬件资源，性能，绩效，能源），以一种已意识到的和数据意识的方式以及3）在较小的范围内构建量的新型型号，以备量构建量的新量。算法 - 硬件共同设计的系统不仅可以加速当前的3D感知算法，而且还提供了计算基板，以便可以将3D感知在未来的应用领域中用作构建范围。该奖项反映了NSF的法定任务，反映了通过评估基金会的cripial crrit and throbirial and tocriatial and tocrit and tocrita和broaditial and throughia and throughia and crarit and cr.

项目成果

From Local to Holistic: Self-supervised Single Image 3D Face Reconstruction Via Multi-level Constraints

• DOI: 10.1109/iros47612.2022.9982284
• 发表时间: 2022-10
• 期刊: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: Yawen Lu;M. Sarkis;N. Bi;G. Lu

Multi-view Geometry Consistency Network for Facial Micro-Expression Recognition From Various Perspectives

• DOI: 10.1109/ijcnn55064.2022.9892565
• 发表时间: 2022-07
• 期刊: 2022 International Joint Conference on Neural Networks (IJCNN)
• 作者: Devarth Parikh;Yawen Lu;N. Kasabov;G. Lu

Object Detection Based on Raw Bayer Images

• DOI: 10.1109/iros55552.2023.10342008
• 发表时间: 2023-10
• 期刊: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: Guoyu Lu