  3D 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 and reduce 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 dToF sensor which uses Spiking Neural Networks (SNN) to process the
SPAD events directly. The proposed SNN is trained and tested on synthetic SPAD
events, and while it offers five times lower precision in depth prediction than
a classic centre-of-mass (CoM) algorithm (applied to histograms of the events),
it achieves similar Mean Absolute Error (MAE) with faster processing speeds and
significantly lower power consumption is anticipated.
