Spiking Neural Networks (SNNs) have garnered widespread interest for their energy efficiency and brain-inspired event-driven properties. While recent methods like Spiking-YOLO have expanded the SNNs to more challenging object detection tasks, they often suffer from high latency and low detection accuracy, making them difficult to deploy on latency sensitive mobile platforms. Furthermore, the conversion method from Artificial Neural Networks (ANNs) to SNNs is hard to maintain the complete structure of the ANNs, resulting in poor feature representation and high conversion errors. To address these challenges, we propose two methods: timesteps compression and spike-time-dependent integrated (STDI) coding. The former reduces the timesteps required in ANN-SNN conversion by compressing information, while the latter sets a time-varying threshold to expand the information holding capacity. We also present a SNN-based ultra-low latency and high accurate object detection model (SUHD) that achieves state-of-the-art performance on nontrivial datasets like PASCAL VOC and MS COCO, with about remarkable 750x fewer timesteps and 30% mean average precision (mAP) improvement, compared to the Spiking-YOLO on MS COCO datasets. To the best of our knowledge, SUHD is the deepest spike-based object detection model to date that achieves ultra low timesteps to complete the lossless conversion.

翻译：脉冲神经网络（SNN）因其能效优势及类脑事件驱动特性而受到广泛关注。尽管Spiking-YOLO等近期方法已将SNN拓展至更具挑战性的目标检测任务，但这些方法往往存在高延迟和低检测精度的问题，难以部署于对延迟敏感的移动平台。此外，从人工神经网络（ANN）到SNN的转换方法难以维持ANN的完整结构，导致特征表示能力差且转换误差高。针对上述挑战，我们提出两种方法：时间步压缩和脉冲时间依赖集成（STDI）编码。前者通过信息压缩减少ANN-SNN转换所需的时间步，后者通过设置时变阈值扩展信息承载容量。我们还提出了基于SNN的超低延迟高精度目标检测模型（SUHD），该模型在PASCAL VOC和MS COCO等具有挑战性的数据集上达到了最优性能，与Spiking-YOLO在MS COCO数据集上的表现相比，时间步数减少约750倍，平均精度均值（mAP）提升30%。据我们所知，SUHD是迄今最深的脉冲目标检测模型，能够以超低时间步实现无损转换。