TDC-Less Direct Time-of-Flight Imaging Using Spiking Neural Networks

Three-dimensional depth sensors using single-photon avalanche diodes (SPADs) are becoming increasingly common in applications such as autonomous navigation and object detection. Recent designs implement on-chip histogramming time-to-digital converters (TDCs) to compress the photon timestamps thereby reducing the bottleneck in the read-out and processing of large volumes of photon data. However, the use of full histogramming with large SPAD arrays poses significant challenges due to the associated demands in silicon area and power consumption. We propose a TDC-less (and histogram-less) direct time-of-flight (dToF) sensor which uses spiking neural networks (SNNs) to process the SPAD events directly. The proposed SNN is trained and tested on synthetic SPAD events, and while it performs at five times lower precision in depth prediction than a classic center-of-mass (CoM) algorithm (applied to histograms of the events), it achieves similar mean absolute error (MAE) with estimated faster processing speeds and estimated significantly lower power consumption.