Model-Driven Quantization for Time Encoding Machines

In conventional analog-to-digital (ADC) conversion, the sampling and quantization steps take place on the time and amplitude axes, respectively. In the case of time encoding machines (TEMs), which convert analog signals into a sequence of time events, sampling and quantization interfere with one another since they both operate on the time axis. Here we introduce a new quantization method for TEMs called QTEM that, due to its model-driven nature, limits the interference of sampling and quantization. We show that existing recovery guarantees don’t apply to QTEM. We provide new guarantees for recovering the input of the QTEM and demonstrate numerically its advantage over conventional TEM quantization.