在像素处理器上实现多阶段类视网膜模型，提升边缘计算中的注意力预测效率。
Programmable Silicon Retina on Pixel Processor Array
- 在像素阵列上实现带空间滤波与增益控制的多级类视网膜处理
- 相比标准动态视觉传感器，显著降低47%事件率，提升13%注意力预测精度
- 适合对带宽敏感的边缘设备，如低功耗智能视觉系统
标准动态视觉传感器通过检测时间对比变化模拟视网膜处理，具备高速度和高动态范围。本文探索在其中引入更多生物启发的处理阶段——包括空间滤波和增益控制——是否能为特定下游任务（如显著性预测）带来优势。我们首次在SCAMP-5像素处理器阵列上实现了多阶段硅视网膜模型，并构建了基于GPU的仿真框架。在视频强度重建与视频显著性预测任务上评估该模型表现：虽然在重建绝对亮度帧方面效果略差，但在显著性预测上相较标准DVS事件表示降低了13%的损失，同时事件率下降约47%。实验采用一个约10万参数的轻量级FireNet风格网络，从事件重建任务迁移至显著性预测。结果表明，硅视网膜的“信息提炼”机制可为下游神经网络提供更高效表示，尤其适用于带宽受限的边缘应用。
原文摘要 · Abstract (English)
Standard dynamic vision sensors approximate retinal processing by detecting temporal contrast changes, offering high speed and high dynamic range. In this work, we explore whether incorporating additional biologically inspired processing stages - specifically spatial filtering and gain control - can offer advantages for certain downstream tasks such as saliency prediction. We present the first implementation of a multi-stage Silicon Retina model on the SCAMP-5 Pixel Processor Array, along with a GPU-based simulation framework. We evaluate the performance of our model on Video Intensity Reconstruction and Video Saliency Prediction. While the bio-inspired model is less effective at reconstructing absolute intensity frames, it achieves a 13\% reduction in saliency prediction loss in comparison to standard DVS event representation, while reducing the event rate by approximately 47\%. These experiments are obtained using a lightweight $\approx 100$k-parameter FireNet-style network, adapted from event-based reconstruction to saliency prediction. These results suggest that the silicon retina's "information distillation" mechanism can achieve a more efficient representation for downstream neural networks, particularly in bandwidth-constrained edge applications.
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