Solving mazes with memristors: a massively-parallel approach

TL;DR

This paper demonstrates that a network of memristors can efficiently solve complex mazes in a massively parallel manner, finding all solutions and sorting them by path length, representing a novel application of memristive networks.

Contribution

It introduces the first use of memristive networks for massively-parallel maze solving, showcasing a new algorithm that leverages memristors' memory for efficient computation.

Findings

Memristive networks can solve mazes in parallel.

All possible maze solutions are found simultaneously.

Solutions are sorted by path length.

Abstract

Solving mazes is not just a fun pastime. Mazes are prototype models in graph theory, topology, robotics, traffic optimization, psychology, and in many other areas of science and technology. However, when maze complexity increases their solution becomes cumbersome and very time consuming. Here, we show that a network of memristors - resistors with memory - can solve such a non-trivial problem quite easily. In particular, maze solving by the network of memristors occurs in a massively parallel fashion since all memristors in the network participate simultaneously in the calculation. The result of the calculation is then recorded into the memristors' states, and can be used and/or recovered at a later time. Furthermore, the network of memristors finds all possible solutions in multiple-solution mazes, and sorts out the solution paths according to their length. Our results demonstrate not only the first application of memristive networks to the field of massively-parallel computing, but also a novel algorithm to solve mazes which could find applications in different research fields.