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

该研究项目设计了软硬件协同机制，将3D感知(点云)算法的执行效率提高一个数量级。要实现这一目标，需要从根本上设计新的算法原语、离线和运行时系统，以及驯服3D感知算法中不规则计算和存储模式的硬件架构。这项研究揭示了由3D感知驱动的新兴领域的下一代软件创新，如自动驾驶、增强/虚拟现实和精准农业。研究议程与教育/外联议程相辅相成。PIs是(1)向罗切斯特中心学区的高中生提供暑期“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机会。该研究项目解决了点云算法与当今硬件架构之间的根本不匹配问题。它们主要针对2D图像和视频处理算法进行了优化，这些算法是在结构化数据上操作的常规模板管道。智能的关键优点是追求新的算法和系统架构，以减少/消除3D感知中的不规则计算和内存访问。技术贡献有三个方面：1)加速点云算法所需的高效、但普遍适用的硬件构建块，2)以应用感知和数据感知的方式动态适应操作约束(例如，硬件资源、性能、能量)的运行时系统，以及3)利用单波束点云中的小数据量的新型按构造高效的3D感知算法。该算法-硬件联合设计系统不仅加速了当前的3D感知算法，还提供了计算基础，使3D感知可以在未来的应用领域广泛用作构建块。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.