BLITZCRANK: Factor Graph Accelerator for Motion Planning
Yuhui Hao, Yiming Gan, Yu Bo, Qiang Liu, Shao-Shan Liu, Yuhao Zhu
Abstract
Factor graph is a graph representing the factorization of a probability distribution function and serves as a perfect abstraction in many autonomous machine computing stacks, such as planning, localization, tracking and control, which are challenging tasks for autonomous systems with real-time and energy constraints.In this paper, we present BLITZCRANK, an accelerator for motion planning algorithms using the abstraction of a factor graph. By formulating motion planning as a factor graph inference, we successfully reduce the scale of the problem and utilize the inherent matrix sparsity. BLITZCRANK is able to realize the user-defined optimal design by finding the optimal order of the factor graph inference. With a domain specific balancing order, BLITZCRANK achieves up to 7.4× speed up and 29.7× energy reduction compared to the software implementation on Intel CPU.