SpOctA: A 3D Sparse Convolution Accelerator with Octree-Encoding-Based Map Search and Inherent Sparsity-Aware Processing

• Published in: Digest of technical papers - IEEE/ACM International Conference on Computer-Aided Design pp. 1 - 9
• Main Authors: Lyu, Dongxu, Lil, Zhenyu, Chen, Yuzhou, Zhang, Jinming, Xu, Ningyi, He, Guanghui
• Format: Conference Proceeding
• Language: English
• Published: IEEE 28.10.2023
• Subjects: 3D Sparse Convolution; Benchmark testing; Convolution; Memory management; Octree Encoding; Point Cloud; Point cloud compression; Redundancy; Sparsity Exploitation; Technological innovation; Three-dimensional displays
• ISSN: 1558-2434
• DOI: 10.1109/ICCAD57390.2023.10323728

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 8.8-21.2× 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 1.1-6.9× speedup with 1.5-3.1× energy efficiency improvement,