Transporter: A 128$\times$4 SPAD Imager with On-chip Encoder for Spiking Neural Network-based Processing

TL;DR

Transporter introduces a novel SPAD imaging sensor with integrated on-chip spike encoding, enabling efficient, real-time, neuromorphic processing by eliminating traditional TDCs and significantly reducing data transfer and processing complexity.

Contribution

The paper presents a new SPAD sensor architecture with integrated spike encoders that preprocess photon data on-chip, enhancing efficiency for neural network-based imaging applications.

Findings

Successfully implemented a 128×4 SPAD sensor with on-chip spike encoding.

Demonstrated real-time, neuromorphic processing capabilities with reduced data overhead.

Enabled phase-to-density spike train conversion for improved neural network training.

Abstract

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.