FLNA: An Energy-Efficient Point Cloud Feature Learning Accelerator with Dataflow Decoupling

Grid-based feature learning network plays a key role in recent point-cloud based 3D perception. However, high point sparsity and special operators lead to large memory footprint and long processing latency, posing great challenges to hardware acceleration. We propose FLNA, a novel feature learning accelerator with algorithm-architecture co-design. At algorithm level, the dataflow-decoupled graph is adopted to reduce 86% computation by exploiting inherent sparsity and concat redundancy. At hardware design level, we customize a pipelined architecture with block-wise processing, and introduce transposed SRAM strategy to save 82.1% access power. Implemented on a 40nm technology, FLNA achieves 13.4 − 43.3× speedup over RTX 2080Ti GPU. It rivals the state-of-the-art accelerator by 1.21× energy-efficiency improvement with 50.8% latency reduction.