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3D object detection using point cloud (PC) data is essential for perception
pipelines of autonomous driving, where efficient encoding is key to meeting
stringent resource and latency requirements. PointPillars, a widely adopted
bird's-eye view (BEV) encoding, aggregates 3D point cloud data into 2D pillars
for fast and accurate 3D object detection. However, the state-of-the-art
methods employing PointPillars overlook the inherent sparsity of pillar
encoding where only a valid pillar is encoded with a vector of channel
elements, missing opportunities for significant computational reduction.
Meanwhile, current sparse convolution accelerators are designed to handle only
element-wise activation sparsity and do not effectively address the vector
sparsity imposed by pillar encoding.

In this paper, we propose SPADE, an algorithm-hardware co-design strategy to
maximize vector sparsity in pillar-based 3D object detection and accelerate
vector-sparse convolution commensurate with the improved sparsity. SPADE
consists of three components: (1) a dynamic vector pruning algorithm balancing
accuracy 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$\times$ speedup and 1.5--12.6$\times$ energy savings compared to the
ideal dense accelerator design. These sparsity-proportional performance gains
equate to 4.1--28.8$\times$ speedup and 90.2--372.3$\times$ energy savings
compared to the counterpart server and edge platforms.