Weightless photonic spike processing of time-of-flight signals with delay learning

Time-of-flight (ToF) signal processing has become increasingly crucial in depth perception applications. We propose a photonic spike processing method for ToF signals based on synaptic delay plasticity, which adjusts the spike timing of encoded signals to achieve low-latency processing without the need for weight loading and control. This method employs photonic neurons that directly encode optical pulses of ToF signals into temporal spike sequences, eliminating the necessity for a time-to-digital converter (TDC). We use tunable optical delay lines to emulate the photonic synaptic regulation of spike timing. In addition, we demonstrate the efficacy of a photonic spiking neural network that trains the synaptic delay parameters using the ModelNet dataset, achieving an accuracy of 96.36%. In experiments, the processing delay of ToF signals is 58.66 ns, representing a reduction of two orders of magnitude compared with traditional TDC-based methods. This approach facilitates applying synaptic diversity in photonic neuromorphic information processing.