用芯片内编码器让单光子传感器直接生成脉冲数据，大幅减少传输和计算负担。
Transporter: A 128$\times$4 SPAD Imager with On-chip Encoder for Spiking Neural Network-based Processing
- 在像素级集成脉冲编码器，用翻转触发器环实现相位到密度的转换。
- 128×4阵列实现实时脉冲神经网络处理，数据量压缩显著，支持边缘实时运算。
- 适合需要低延迟、高能效的智能成像系统，如自动驾驶、生物显微观测。
单光子雪崩二极管（SPAD）广泛应用于时间分辨成像。然而，传统架构依赖时间-数字转换器（TDC）与直方图处理，导致数据传输与计算压力大。先前基于递归神经网络的工作已实现无直方图处理。为进一步突破限制，本文提出一种新范式：通过在感测端集成脉冲编码器，消除TDC，实现光子到达事件的原位预处理。该方法显著压缩数据，降低复杂度，并保持实时边缘处理能力。专用脉冲编码器将多个激光重复周期折叠，将基于相位的脉冲序列转化为适合脉冲神经网络处理与通过时间反向传播训练的密度型脉冲序列。作为概念验证，本文设计并实现了Transporter——一款128×4的SPAD传感器，每个像素配备基于D触发器环的脉冲编码器，专用于智能主动时间分辨成像。该工作为更高效、类脑化的SPAD成像系统提供了可行路径，显著降低数据开销并提升实时处理性能。
原文摘要 · Abstract (English)
Single-photon avalanche diodes (SPADs) are widely used today in time-resolved imaging applications. However, traditional architectures rely on time-to-digital converters (TDCs) and histogram-based processing, leading to significant data transfer and processing challenges. Previous work based on recurrent neural networks has realized histogram-free processing. To further address these limitations, we propose a novel paradigm that eliminates TDCs by integrating in-sensor spike encoders. This approach enables preprocessing of photon arrival events in the sensor while significantly compressing data, reducing complexity, and maintaining real-time edge processing capabilities. A dedicated spike encoder folds multiple laser repetition periods, transforming phase-based spike trains into density-based spike trains optimized for spiking neural network processing and training via backpropagation through time. As a proof of concept, we introduce Transporter, a 128$\times$4 SPAD sensor with a per-pixel D flip-flop ring-based spike encoder, designed for intelligent active time-resolved imaging. This work demonstrates a path toward more efficient, neuromorphic SPAD imaging systems with reduced data overhead and enhanced real-time processing.
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