  Building domain-specific architectures for autonomous aerial robots is
challenging due to a lack of systematic methodology for designing onboard
compute. We introduce a novel performance model called the F-1 roofline to help
architects understand how to build a balanced computing system for autonomous
aerial robots considering both its cyber (sensor rate, compute performance) and
physical components (body-dynamics) that affect the performance of the machine.
We use F-1 to characterize commonly used learning-based autonomy algorithms
with onboard platforms to demonstrate the need for cyber-physical co-design. To
navigate the cyber-physical design space automatically, we subsequently
introduce AutoPilot. This push-button framework automates the co-design of
cyber-physical components for aerial robots from a high-level specification
guided by the F-1 model. AutoPilot uses Bayesian optimization to automatically
co-design the autonomy algorithm and hardware accelerator while considering
various cyber-physical parameters to generate an optimal design under different
task level complexities for different robots and sensor framerates. As a
result, designs generated by AutoPilot, on average, lower mission time up to 2x
over baseline approaches, conserving battery energy.
