Hardware Architecture of Graph Neural Network-enabled Motion Planner (Invited Paper)

Abstract

Motion planning aims to find a collision-free trajectory from the start to goal configurations of a robot. As a key cognition task for all the autonomous machines, motion planning is fundamentally required in various real-world robotic applications, such as 2-D/3-D autonomous navigation of unmanned mobile and aerial vehicles and high degree-of-freedom (DoF) autonomous manipulation of industry/medical robot arms and graspers.Motion planning can be performed using either non-learning- based classical algorithms or learning-based neural approaches. Most recently, the powerful capabilities of deep neural networks (DNNs) make neural planners become very attractive because of their superior planning performance over the classical methods. In particular, graph neural network (GNN)-enabled motion planner has demonstrated the state-of-the-art performance across a set of challenging high-dimensional planning tasks, motivating the efficient hardware acceleration to fully unleash its potential and promote its widespread deployment in practical applications.To that end, in this paper we perform preliminary study of the efficient accelerator design of the GNN-based neural planner, especially for the neural explorer as the key component of the entire planning pipeline. By performing in-depth analysis on the different design choices, we identify that the hybrid architecture, instead of the uniform sparse matrix multiplication (SpMM)-based solution that is popularly adopted in the existing GNN hardware, is more suitable for our target neural explorer. With a set of optimization on microarchitecture and dataflow, several design challenges incurred by using hybrid architecture, such as extensive memory access and imbalanced workload, can be efficiently mitigated. Evaluation results show that our proposed customized hardware architecture achieves order-of-magnitude performance improvement over the CPU/GPU-based implementation with respect to area and energy efficiency in various working environments.