Automatic Domain-Specific SoC Design for Autonomous Unmanned Aerial Vehicles

Building domain-specific accelerators is becoming increasingly paramount to meet the high-performance requirements under stringent power and real-time constraints. However, emerging application domains like autonomous vehicles are complex systems, where the constraints extend beyond just the computing stack. Manually selecting and navigating the design space to design custom and efficient domain-specific SoCs (DSSoC) is tedious and expensive. As such, there is a need for automated DSSoC design methodologies. In this paper, we use agile and autonomous UAVs as a case study for understanding how to automate the design of domain-specific SoCs for autonomous vehicles. Architecting a UAV DSSoC requires considering parameters such as sensor rate, compute throughput, and other physical characteristics (e.g., payload weight, thrust-to-weight ratio) that affect overall performance. Iterating over the many component choices results in a combinatorial explosion of the number of possible combinations: from 10s of thousands to billions, depending on implementation details. To navigate the DSSoC design space efficiently, we introduce \emph{AutoPilot}, a systematic methodology for automatically designing DSSoC for autonomous UAVs. AutoPilot uses machine learning to navigate the large DSSoC design space and automatically select a combination of autonomy algorithm and hardware accelerator while considering the cross-product effect across different UAV components. \autop consistently outperforms general-purpose hardware selections like Xavier NX and Jetson TX2, as well as dedicated hardware accelerators built for autonomous UAVs. DSSoC designs generated by \autop increase the number of missions on average by up to 2.25x, 1.62x and 1.43x for nano, micro, and mini-UAVs, respectively, over baselines. We also discuss how \autop can be extended to other related autonomous vehicles using the same set of principles.

• Hardware and Architecture

• Robotics