Co-Designing Perception-Based Autonomous Systems on CPU-GPU Platforms

Abstract

Perception-based autonomous system design methods are widely adopted in various domains like transportation, industrial robotics, etc. However, attaining safe and predictable execution in such systems depends on the platform-level integration of perception and control tasks. This letter presents a novel methodology to co-optimize these tasks, assuming a CPU-GPU-based real-time platform, a common choice of compute resource in this domain. Unlike the traditional methods that separately address AI-based sensing and control concerns, we consider that the overall performance of the system depends on the inferencing accuracy of the perception tasks and the performance of the control tasks iteratively executing in a feedback loop. We propose a design-space exploration methodology that considers the above concern and validates the same on an autonomous driving use case using a novel simulation setup.