Minimum Bitrate Neuromorphic Encoding for Continuous-Time Gauss-Markov Processes

TL;DR

This paper investigates the minimum data rate required for continuous-time system tracking using neuromorphic event-based sensing, bridging control theory and information theory to establish fundamental limits.

Contribution

It introduces a novel theoretical framework connecting continuous-time dynamics with causal rate distortion theory for event-based sensing.

Findings

Derived new lower bounds for event-based sensing data rates

Compared bounds with existing sensing schemes

Provided insights into optimal encoding strategies

Abstract

In this work, we study minimum data rate tracking of a dynamical system under a neuromorphic event-based sensing paradigm. We begin by bridging the gap between continuous-time (CT) system dynamics and information theory's causal rate distortion theory. We motivate the use of non-singular source codes to quantify bitrates in event-based sampling schemes. This permits an analysis of minimum bitrate event-based tracking using tools already established in the control and information theory literature. We derive novel, nontrivial lower bounds to event-based sensing, and compare the lower bound with the performance of well-known schemes in the established literature.