Neuromorphic Sensing Meets Unlimited Sampling

Abstract

Unlimited sampling is a computational sensing paradigm for high-dynamic range (HDR) acquisition of continuous-time signals. In standard analog-to-digital converters (ADC), a fixed input dynamic range is accommodated by clipping or saturation of the signal outside the dynamic range. In unlimited sampling, signal that lies outside the fixed dynamic range is folded back, thereby preserving the signal dynamic range. The folding is achieved using a self-reset ADC (SR-ADC), which performs a continuous-time modulo operation. In this paper, we use a neuromorphic encoder, which is an opportunistic and event-driven sampling device, and propose a new technique for unlimited sampling. We show that the neuromorphic encoder folds the signal that lies outside the dynamic range and simultaneously records a compressed representation of the error signal. Unlimited sampling is achieved by measuring the folded signal. We analyze sampling and reconstruction of finite-energy, bandlimited signals and show that perfect reconstruction is possible using uniform samples acquired at the Nyquist rate of the signal. The reconstruction technique operates in real-time, and can be readily extended to larger classes of continuous-time signals. We demonstrate the performance of our technique and report comparisons with state-of-the-art techniques using simulations.