CAREER: SHF: Chimp: Algorithm-Hardware-Automation Co-Design Exploration of Real-Time Energy-Efficient Motion Planning

职业：SHF：黑猩猩：实时节能运动规划的算法-硬件-自动化协同设计探索

基本信息

批准号：
2239945
负责人：
Bo Yuan
金额：
$ 50万
依托单位：
Rutgers University New Brunswick
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-01-01 至 2027-12-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239945&HistoricalAwards=false
关键词：
CAREER SHF Chimp Algorithm Hardware

项目摘要

As the fundamental and critical robotic task for planning and deciding the actions of robots, motion planning is widely desired in many real-world applications, such as autonomous driving, in-warehouse package handling, assisted surgery etc. To date, there exists an increasing performance gap between the intensive computation of modern motion planning workloads and the insufficient support from general-purpose hardware, calling for efficient hardware acceleration to realize real-time energy-efficient high-quality planning. This project proposes Chimp, a cross-layer co-design framework for highly efficient motion planning processor. Chimp aims to develop a new design paradigm that can efficiently integrate domain expertise into learning-based motion planning, improving the planning reliability and performance. This project will significantly promote the intelligence and durability of modern autonomous systems, enhancing the economic opportunities in many fields such as autonomous driving, smart manufacturing, and intelligent healthcare. This project will enrich the curriculum of the university and promote the involvement of students from underrepresented minority groups, undergraduates and K-12 students in the STEM fields.This project aims to perform algorithm-hardware-automation co-exploration to simultaneously enable high planning performance and high hardware performance. It delivers innovations at three levels: (1) it develops key design principles that can guide the efficient integration of domain expertise to the construction of high-performance learning-based motion planners in complex physical-world settings and resource-constrained scenarios; (2) it builds new hardware primitives that specifically support the unique computing patterns in motion planning. It also proposes a series of optimization techniques for dataflow and microarchitecture, improving hardware efficiency and system utilization; and (3) it offers automatic design, mapping and evaluation of the motion planning model and hardware with different algorithmic, architectural and application constraints and budgets, enabling the improved efficiency of design flow and better exploration of design space. Both software and hardware implementation and evaluation will be performed on robotic simulators, Field-programmable gate array boards and real-world robots in different working environments. The research outcomes of this project will advance various technical fields, such as computing hardware, robotics and machine learning.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.

作为规划和决定机器人动作的基本且关键的机器人任务，运动规划在许多实际应用中被广泛需要，例如自动驾驶、仓库内包裹处理、辅助手术等。迄今为止，运动规划在许多实际应用中得到了广泛的应用。现代运动规划工作负载的密集计算与通用硬件支持不足之间的性能差距，需要高效的硬件加速来实现实时节能的高质量规划。该项目提出了 Chimp，一种用于高效运动规划处理器的跨层协同设计框架。 Chimp 旨在开发一种新的设计范例，可以有效地将领域专业知识集成到基于学习的运动规划中，从而提高规划的可靠性和性能。该项目将显着提升现代自动驾驶系统的智能性和耐用性，增强自动驾驶、智能制造、智能医疗等多个领域的经济机会。该项目将丰富大学的课程，并促进少数族裔学生、本科生和 K-12 学生参与 STEM 领域。该项目旨在进行算法-硬件-自动化协同探索，同时实现高规划性能和高硬件性能。它在三个层面上提供创新：（1）它开发了关键的设计原则，可以指导领域专业知识的有效集成，以在复杂的物理世界环境和资源受限的场景中构建高性能的基于学习的运动规划器； (2)它构建了新的硬件原语，专门支持运动规划中的独特计算模式。还提出了一系列针对数据流和微架构的优化技术，提高硬件效率和系统利用率； (3)它提供了具有不同算法、架构和应用约束和预算的运动规划模型和硬件的自动设计、映射和评估，从而提高了设计流程的效率并更好地探索设计空间。软件和硬件的实现和评估都将在机器人模拟器、现场可编程门阵列板和不同工作环境中的真实机器人上进行。该项目的研究成果将推动计算硬件、机器人和机器学习等各个技术领域的发展。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。