Spiking Neural Networks for Active Time-Resolved SPAD Imaging

Single-photon avalanche diodes (SPADs) are detectors capable of capturing single photons and of performing photon counting. SPADs have an exceptional temporal resolution and are thus highly suitable for time-resolved imaging applications. Applications span from biomedical research to consumers with SPADs integrated in smartphones and mixed-reality headsets. While conventional SPAD imaging systems typically employ photon time-tagging and histogram-building in the workflow, the pulse signal output of a SPAD naturally lends itself as input to spiking neural networks (SNNs). Leveraging this potential, SNNs offer real-time, energy-efficient, and intelligent processing with high throughput. In this paper, we propose two SNN frameworks, namely the Transporter SNN and the Reversed Start-stop SNN, along with corresponding hardware schemes for active time-resolved SPAD imaging. These frameworks convert phase-coded spike trains into density- and interspike-interval-coded ones, enabling training with rate-based warm-up and Surrogate Gradient. The SNNs are evaluated on fluorescence lifetime imaging. The results demonstrate that the accuracy of shallow SNNs is on par with established benchmarks. Our vision is to integrate SNNs in SPAD sensors and to explore advanced SNNs within the proposed schemes for high-level applications.