Abstract
This paper addresses Cellular Automata (CA) based algorithm implementations using circuits with memory resistors (memristors). Memristors are two-terminal passive nonvolatile resistance switching devices whose unique adaptive properties are suitable for massively parallel computational purposes. The sparse nature of computations using network configurations of memristors resembles certain operational features and computing capabilities of CA. Here a memristive CA capable of detecting the shortest path between given nodes of a mesh with weighted edges is proposed. Simulation results are in absolute agreement with the solutions given by the corresponding CA-based algorithmic approach. The proposed memristive CA circuit structure is also used for the effective solution of the traveling salesman problem.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Ioannidis, K., Sirakoulis, G.C., Andreadis, I.: A path planning method based on Cellular Automata for Cooperative Robots. Applied Artificial Intelligence 25(8), 721–745 (2011)
Golzari, S., Meybodi, M.R.: A Maze Routing Algorithm Based on Two Dimensional Cellular Automata. In: El Yacoubi, S., Chopard, B., Bandini, S. (eds.) ACRI 2006. LNCS, vol. 4173, pp. 564–570. Springer, Heidelberg (2006)
Adamatzky, A.I.: Computation of shortest path in cellular automata. Math. Comput. Modelling 23(4), 105–113 (1996)
Vourkas, I., Sirakoulis, G.C.: FPGA based cellular automata for environmental modeling. In: 19th IEEE Int. Conf. Electronics, Circuits, and Systems (ICECS 2012), pp. 93–96. IEEE Press, Seville (2012)
Strukov, D.B., Snider, G.S., Stewart, D.R., Williams, R.S.: The missing memristor found. Nature 453, 80–83 (2008)
Vourkas, I., Sirakoulis, G.C.: Recent progress and patents on computational structures and methods with memristive devices. Recent Patents on Electrical & Electronic Engineering 6(2), 101–116 (2013)
Yang, J.J., Strukov, D.B., Stewart, D.R.: Memristive devices for computing. Nat. Nano 8, 13–24 (2013)
Adamatzky, A., Chua, L. (eds.): Memristor Networks. Springer International Publishing, Switzerland (2014)
Pershin, Y., Di Ventra, M.: Solving mazes with memristors: A massively parallel approach. Phys. Rev. E 84, 046703 (2011)
Ye, Z., Wu, S.H.M., Prodromakis, T.: Computing shortest paths in 2D and 3D memristive networks. arXiv:1303.3927 (2013)
Itoh, M., Chua, L.O.: Memristor cellular automata and memristor discrete-time cellular neural networks. Int. J. Bifurcation Chaos 19(11), 3605–3656 (2009)
Chua, L.O.: Memristor - The missing circuit element. IEEE Trans. Circuit Theory 18(5), 507–519 (1971)
Prodromakis, T., Toumazou, C., Chua, L.: Two centuries of memristors. Nature Materials 11, 478–481 (2012)
Chua, L.O., Kang, S.M.: Memristive devices and systems. Proc. IEEE 64(2), 209–223 (1976)
Vourkas, I., Sirakoulis, G.C.: A novel design and modeling paradigm for memristor-based crossbar circuits. IEEE Trans. Nanotechnol. 11(6), 1151–1159 (2012)
Easy Java Simulations (EJS), http://fem.um.es/Ejs/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Stathis, D., Vourkas, I., Sirakoulis, G.C. (2014). Shortest Path Computing Using Memristor-Based Circuits and Cellular Automata. In: Wąs, J., Sirakoulis, G.C., Bandini, S. (eds) Cellular Automata. ACRI 2014. Lecture Notes in Computer Science, vol 8751. Springer, Cham. https://doi.org/10.1007/978-3-319-11520-7_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-11520-7_41
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11519-1
Online ISBN: 978-3-319-11520-7
eBook Packages: Computer ScienceComputer Science (R0)