A 227.1 TOPS/W High Energy-Efficiency PNN-Based 3D Object Recognition Processor with Spiking Neural Network for Edge Device

This paper proposes a high-efficiency spiking neural network (SNN) processor for point-cloud neural network (PNN) based 3D object recognition on edge devices. PNN independently process points, leading to redundant computations and low energy efficiency. SNN leverage the characteristics of accumulation operations and the properties of Leaky Integrate-and-Fire (LIF) activation, to optimize efficiency. A novel 3D object recognition processor maximizes the characteristics of SNN by introducing two key features: 1) intra-timestep computation skipping and 2) timestep reduction via inter-timestep correlation analysis. The Event-driven Bit-serial Processing Unit (EBPU) utilizes intratimestep computation skipping for up to 7.2× higher energyefficiency. The Output Spike Precognition Unit (OSPU) enhances inter-timestep correlation analysis for 65% improvement in throughput. Consequently, the proposed processor achieves an energy efficiency of 227.1 TOPS/W for 3D object classification, 218.1 TOPS/W for part segmentation, and 92.5 TOPS/W for semantic segmentation, achieving up to 22.9× higher energy efficiency than the conventional state-of-the-art PNN accelerators