IDEA-GP: Instruction-Driven Architecture with Efficient Online Workload Allocation for Geometric Perception

The algorithmic complexity of robotic systems presents significant challenges to achieving generalized acceleration in robot applications. On the one hand, the diversity of operators and computational flows within similar task categories prevents the reuse of specialized computational units. On the other hand, task variations and environmental dynamics can cause workload fluctuations, leading to inefficient resource utilization.

This paper focuses on the geometric perception capability of robots, taking localization and mapping as the basic applications, and proposes IDEA-GP, an Instruction-Driven Architecture with Efficient online workload Allocation for Geometric Perception. Built around an array of general computational units designed for spatial positioning representations, IDEA-GP supports a wide range of robot pose-related computational tasks. IDEA-GP employs a compiler to perform online workload analysis and resource allocation. It generates instructions tailored to processing elements (PEs) to schedule computations, thereby accelerating optimization problems and enhancing geometric perception performance. Deployed on the ZCU102 evaluation board, IDEA-GP demonstrates an average speedup of 7.5× over the Intel CPU and 19.7× over the ARM CPU in Simultaneous Localization and Mapping (SLAM) tasks, and a 16.4× speedup over the Intel CPU and 41.6× over the ARM CPU in Structure from Motion (SfM) tasks.