Abstract

Memristive technology is a promising game-changer in computers and electronics. In this paper, a system exploring the optimal paths through a maze, utilizing a memristor-based setup, is developed and concreted on a FPGA (field-programmable gate array) device. As a memristor, a digital emulator has been used. According to the proposed approach, the memristor is used as a delay element, further configuring the test graph as a memristor network. A parallel algorithm is then applied, successfully reducing computing time and increasing the system’s efficiency. The proposed system is simple, easy to scale up and capable of implementing different graph configurations. The operation of the algorithm in the MATLAB (matrix laboratory) programming enviroment is checked beforehand and then exported to two different Intel FPGAs: a DE0-Nano board and an Arria 10 GX 220 FPGA. In both cases, reliable results are obtained quickly and conveniently, even for the case of a 300 × 300 nodes maze.

Highlights

  • GERARD: GEneral RApid ResolutionSince ancient times, humankind has tried to solve labyrinths or mazes

  • The paradigm of maze solving is found in the Greek myth of Ariadne, who used a thread to help Theseus get out of Minotaur’s labyrinth

  • The proposed method for the maze solver is based on representing the maze as a matrix of nodes connected through memristors (Figure 1) to its four nearest neighbors, forming a memristive grid as in the original paper of Pershin et al [8]

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Summary

Introduction

GERARD: GEneral RApid ResolutionSince ancient times, humankind has tried to solve labyrinths or mazes. Some algorithms obtain an exit path, while others optimize it by finding the shortest one One of the latter is the Dijkstra algorithm [4] that calculates all possible paths to reach a final node beginning from an initial one and compares the total cost of all of them, eventually keeping with the shortest. This algorithm, as well as all of its alternatives, quantum computing excluded [5], requires a long computation time when dealing with complex graphs. Parallel computing becomes a very good alternative in reducing computing time and further improves efficiency [6,7,8]

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