Sensitivity of a Chaotic Logic Gate

TL;DR

This paper investigates the extreme sensitivity of chaotic logic gates to noise and parameter variations, revealing fundamental limitations in their reliability for computing due to exponential error growth.

Contribution

It provides a detailed analysis of noise sensitivity in chaotic logic gates and discusses potential improvements, highlighting the challenges in practical chaotic computing.

Findings

Chaogates' chaotic regions align with high-error zones.

Error grows exponentially within 4-10 iterations.

Fundamental limitations of chaotic computing are identified.

Abstract

Chaotic logic gates or `chaogates' are a promising mixed-signal approach to designing universal computers. However, chaotic systems are exponentially sensitive to small perturbations, and the effects of noise can cause chaotic computers to fail. Here, we examine the sensitivity of a simulated chaogate to noise and other parameter variations (such as differences in supply voltage). We find that the regions in parameter space corresponding to chaotic dynamics coincide with the regions of maximum error in the computation. Further, this error grows exponentially within 4-10 iterations of the chaotic map. As such, we discuss the fundamental limitations of chaotic computing, and suggest potential improvements. Our Python simulation methods are open-source and available at https://github.com/Noeloikeau/chaogate.