SPADE: Sparse Pillar-based 3D Object Detection Accelerator for Autonomous Driving

HPCA 2024 [paper]

This paper propose SPADE, an algorithm-hardware co- design strategy to maximize vector sparsity in pillar-based 3D object detection and accelerate vector-sparse convolution com- mensurate with the improved sparsity. SPADE consists of three components: (1) a dynamic vector pruning algorithm balancing ac- curacy and computation savings from vector sparsity, (2) a sparse coordinate management hardware transforming 2D systolic array into a vector-sparse convolution accelerator, and (3) sparsity- aware dataflow optimization tailoring sparse convolution schedules for hardware efficiency. Taped-out with a commercial technology, SPADE saves the amount of computation by 36.3–89.2% for representative 3D object detection networks and benchmarks, leading to 1.3–10.9× speedup and 1.5–12.6× energy savings compared to the ideal dense accelerator design. These sparsity- proportional performance gains equate to 4.1–28.8× speedup and 90.2–372.3× energy savings compared to the counterpart server and edge platforms.