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

Rutgers University New Brunswick, New Brunswick NJ

Investigators

Abstract

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 undergraduate 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.