Jigsaw: Taming BEV-centric Perception on Dual-SoC for Autonomous Driving

Real-time perception is important for autonomous driving. We observe an emerging trend using one large and critical fusion-based Bird’s-Eye-View (BEV) Deep Neural Network (DNN) model to perform core perception tasks. It collaborates with a few auxiliary Perspective-View (PV) models, forming a BEV-centric paradigm. Organizing the BEV and PV models respecting their distinct real-time requirements becomes challenging, especially on the state-of-the-practice GPU-integrated dual System-on-Chip (SoC) platform. It remains unclear how to appropriately allocate the separated GPU resource to BEV and PV models, satisfying their distinct real-time requirements with latency predictability. No public solution has been proposed for this emerging software-hardware combination.This paper explores parallelism and a timeslot-filling mechanism to organize tasks. We propose Jigsaw, a specialized execution timeline management framework for BEV-centric perception on dual-SoC. First, it exploits component parallelism to carefully place BEV model components and reduce BEV model latency. Second, we recognize two types of idle GPU timeslots left by a parallelized BEV model. The stable timeslot can offer hard real-time guarantee for PV models, while the unstable timeslot could only provide soft real-time capability. Therefore, Jigsaw schedules PV models by timeslot filling to ensure latency predictability of BEV model and deadline satisfaction of PV models. The framework is implemented in compliance with the practical computing stack in modern autonomous vehicles. It is evaluated on a dual-SoC prototype connected via a PCIe bus. Results show that it achieves $1.52-1.63 \times$ speedup for the BEV model compared to no parallelism. It also ensures deadline satisfaction for PV models without interference in BEV model latency predictability.