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: 2126643
• 负责人: Guoyu Lu
• 金额: $ 21.49万
• 依托单位: Rochester Institute of Tech
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2126643&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硬件协作机制，以将3D感知（点云）算法的执行效率提高一个数量级。实现这一目标需要设计全新的算法原语、离线和运行时系统以及硬件架构，以驯服3D感知算法中的不规则计算和内存模式。这项研究将在3D感知驱动的新兴领域（如自动驾驶、增强/虚拟现实和精准农业）开启下一代软件创新。研究议程由教育/外联议程补充。PI是（1）为来自罗切斯特中心学区的高中生提供夏季“计算入门”课程，（2）通过体验3D传感和研究活动，吸引学生参加RIT的国家聋人技术学院项目，（3）在UR和RIT中引入关于3D感知的新课程/模块，包括算法和硬件系统，以及（4）为本科生提供在新兴应用领域和硬件加速方面的实践经验的机会。本研究项目解决了点云算法中的不规则性与当今硬件架构之间的根本不匹配，其主要针对2D图像和视频处理算法进行优化，所述2D图像和视频处理算法是对结构化数据进行操作的常规模板流水线。关键的智力价值是追求新的算法和系统架构，减少/消除3D感知中的不规则计算和内存访问。技术贡献是三方面的：1）加速点云算法所需的高效但普遍适用的硬件构建块，2）动态适应操作约束的运行时系统（例如，硬件资源、性能、能量），以及3）利用单波束点云中的小数据量的新型构造高效3D感知算法。该算法-硬件协同设计的系统不仅加速了当前的3D感知算法，而且还提供了一个计算基础，使3D感知可以广泛地用作未来应用领域的构建块。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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