  Point-cloud-based 3D perception has attracted great attention in various
applications including robotics, autonomous driving and AR/VR. In particular,
the 3D sparse convolution (SpConv) network has emerged as one of the most
popular backbones due to its excellent performance. However, it poses severe
challenges to real-time perception on general-purpose platforms, such as
lengthy map search latency, high computation cost, and enormous memory
footprint. In this paper, we propose SpOctA, a SpConv accelerator that enables
high-speed and energy-efficient point cloud processing. SpOctA parallelizes the
map search by utilizing algorithm-architecture co-optimization based on octree
encoding, thereby achieving 8.8-21.2x search speedup. It also attenuates the
heavy computational workload by exploiting inherent sparsity of each voxel,
which eliminates computation redundancy and saves 44.4-79.1% processing
latency. To optimize on-chip memory management, a SpConv-oriented non-uniform
caching strategy is introduced to reduce external memory access energy by 57.6%
on average. Implemented on a 40nm technology and extensively evaluated on
representative benchmarks, SpOctA rivals the state-of-the-art SpConv
accelerators by 1.1-6.9x speedup with 1.5-3.1x energy efficiency improvement.
