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 densitybased spike trains optimized for spiking neural network processing and training via backpropagation through time. As a proof of concept, we introduce Transporter, a 128×4 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.