PillarAcc: Sparse PointPillars Accelerator for Real-Time Point Cloud 3D Object Detection on Edge Devices
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3D object detection using point cloud (PC) data is vital for autonomous driving perception pipelines, 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 high-accuracy 3D object detection. However, most state-of-the-art methods employing PointPillar overlook the inherent sparsity of pillar encoding, missing opportunities for significant computational reduction. In this study, we propose a groundbreaking algorithm-hardware co-design that accelerates sparse convolution processing and maximizes sparsity utilization in pillar-based 3D object detection networks. We investigate sparsification opportunities using an advanced pillar-pruning method, achieving an optimal balance between accuracy and sparsity. We introduce PillarAcc, a state-of-the-art sparsity support mechanism that enhances sparse pillar convolution through linear complexity input-output mapping generation and conflict-free gather-scatter memory access. Additionally, we propose dataflow optimization techniques, dynamically adjusting the pillar processing schedule for optimal hardware utilization under diverse sparsity operations. We evaluate PillarAcc on various cutting-edge 3D object detection networks and benchmarks, achieving remarkable speedup and energy savings compared to representative edge platforms, demonstrating record-breaking PointPillars speed of 500FPS with minimal compromise in accuracy.
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