Dimitrios Stathis
ABSTRACT
This paper presents a novel circuit-level Cellular Automata (CA)-inspired computational scheme capable of executing computations within memory. The proposed computing structures exploit the threshold-based resistance switching behavior of memristors and of their multi-state composite components. Array-like circuit structures with memristors are designed and their ability to efficiently solve the classic "bin packing" problem is verified via a simulation-based validation using a published memristor device model. A fundamental memristive cell which implements a one-dimensional CA rule is described in detail and then employed in a sophisticated two-dimensional array able to execute the "First-Fit" (decreasing) bin packing algorithm.
- Lewis, R. 2009. A General-Purpose Hill-Climbing Method for Order Independent Minimum Grouping Problems: A Case Study in Graph Colouring and Bin Packing. Comput. Oper. Res. 36, 7 (July 2009), 2295--2310. DOI= http://dx.doi.org/10.1016/j.cor.2008.09.004 Google Scholar
Digital Library
- Coffman Jr., E. G., Csirik, J., Galambos, G., Martello, and S., Vigo, D. 2013. Bin Packing Approximation Algorithms: Survey and Classification. In Handbook of Combinatorial Optimization, P M. Pardalos, D.-Z. Du, and R. L. Graham, Ed. Springer, New York, NY, 455--531. DOI= http://dx.doi.org/10.1007/978-1-4419-7997-1_35Google Scholar
- Johnson, D. S. 1974. Fast Algorithms for Bin Packing. J. Comput. Syst. Sci. 8, 3 (June 1974), 272--314. DOI= http://dx.doi.org/10.1016/S0022-0000(74)80026-7 Google ScholarDigital Library
- Chopard, B. 2012. Cellular Automata Modeling of Physical Systems. In Computational Complexity, R. A. Meyers, Ed. Springer, New York, NY, 407--433. DOI= http://dx.doi.org/10.1007/978-1-4614-1800-9_27Google Scholar
- Adamatzky, A.I. 1996. Computation of shortest path in cellular automata. Math. Comput. Modell. 23, 4, (February 1996), 105--113. DOI= http://dx.doi.org/10.1016/0895-7177(96)00006-4 Google ScholarDigital Library
- Ioannidis, K., Sirakoulis, G. Ch., and Andreadis, I. 2011. A path planning method based on Cellular Automata for Cooperative Robots. Appl. Artif. Intell. 25, 8, (September 2011), 721--745. DOI= http://dx.doi.org/10.1080/08839514.2011.606767 Google ScholarDigital Library
- Golzari, S., and Meybodi, M.R. 2006. A Maze Routing Algorithm Based on Two Dimensional Cellular Automata. In Proceedings of the 7th international conference on Cellular Automata for Research and Industry (Perpignan, France, September 20-23, 2006). In Lect. Notes Comput. Sc. 4173, S. El Yacoubi, B. Chopard, S. Bandini, Ed. Springer Berlin Heidelberg, 564--570. DOI= http://dx.doi.org/10.1007/11861201_65 Google ScholarDigital Library
- Charalampous, K., Amanatiadis, A., and Gasteratos, A. 2012. Efficient Robot Path Planning in the Presence of Dynamically Expanding Obstacles. In Proceedings of the 10th international conference on Cellular Automata for Research and Industry (Santorini Island, Greece, September 24-27, 2012). In Lect. Notes Comput. Sc. 7495, G. Ch. Sirakoulis, S. Bandini, Ed. Springer Berlin Heidelberg, 330--339. DOI= http://dx.doi.org/10.1007/978-3-642-33350-7_34Google Scholar
- Gordillo, J. L., and Luna, J. V. 1994. Parallel sort on a linear array of cellular automata. In Proccedings of 1994 IEEE International Conference on Systems, Man, and Cybernetics, Humans, Information and Technology (San Antonio, TX, October 2-5, 1994). 1903--1907 vol.2. DOI= http://dx.doi.org/10.1109/ICSMC.1994.400129Google Scholar
- Vourkas, I., and Sirakoulis, G. Ch. 2012. FPGA based cellular automata for environmental modeling. In Proceedings of 19th IEEE Int. Conf. Electronics, Circuits, and Systems (Seville, Spain, December 9-12, 2012). ICECS '12. 93--96. DOI= http://dx.doi.org/10.1109/ICECS.2012.6463791Google Scholar
- Halbach, M., and Hoffmann, R. 2004. Implementing cellular automata in FPGA logic. In Proceedings of 18th International Parallel and Distributed Processing Symposium. (Santa Fe, New Mexico, April 26-30, 2004). IPDPS '04. DOI= http://dx.doi.org/10.1109/IPDPS.2004.1303324Google Scholar
- Strukov, D. B., Snider, G. S., Stewart, D. R., and Williams, R.S. 2008. The missing memristor found. Nature 453, (May 2008), 80--83. DOI= http://dx.doi.org/10.1038/nature06932Google Scholar
- Vourkas, I., and Sirakoulis, G. Ch. 2013. Recent progress and patents on computational structures and methods with memristive devices. Recent Patents on Electrical & Electronic Engineering 6, 2, (August 2013), 101--116. DOI= http://dx.doi.org/10.2174/22131116113069990004Google ScholarDigital Library
- Yang, J. J., Strukov, D. B., and Stewart, D. R. 2013. Memristive devices for computing. Nat. Nanotechnol. 8, (January 2013), 13--24. DOI= http://dx.doi.org/10.1038/nnano.2012.240Google Scholar
- Zhao, W., Querlioz, D. Klein, J.-O. Chabi, D. and Chappert, C. 2012. Nanodevice-based Novel Computing Paradigms and the Neuromorphic Approach. In Proceedings of 2012 IEEE International Symposium on Circuits and Systems (Seoul, South Korea, May 20-23 2012). ISCAS '12. 2509--2512. DOI= http://dx.doi.org/10.1109/ISCAS.2012.6271812Google Scholar
- Ye, Z., Wu, S. H. M., and Prodromakis, T. 2013. Computing shortest paths in 2D and 3D memristive networks http://arxiv.org/abs/1303.3927Google Scholar
- Di Ventra, M., and Pershin, Y. V. 2013. The parallel approach. Nat. Phys. 9, (April 2013), 200--202. DOI= http://dx.doi.org/10.1038/nphys2566Google Scholar
- Chua, L. O., and Kang, S. M. 1976. Memristive devices and systems. Proc. IEEE 64, 2, (February 1976), 209--223. DOI= http://dx.doi.org/10.1109/PROC.1976.10092Google Scholar
- Vourkas, I., Batsos, A., and Sirakoulis, G. Ch. 2013. SPICE modeling of nonlinear memristive behavior. Int. J. Circ. Theor. Appl. DOI= http://dx.doi.org/10.1002/cta.1957Google ScholarDigital Library
- Vourkas, I., and Sirakoulis, G. Ch. 2013. On the Analog Computational Characteristics of Memristive Networks. In Proceedings of 20th IEEE International Conference on Electronics, Circuits, and Systems (Abu Dhabi, UAE, December 8-11, 2013). ICECS '13. 309--312. DOI= http://dx.doi.org/10.1109/ICECS.2013.6815416Google ScholarCross Ref
- Easy Java Simulations (EJS). (2014). {Online}. Available: http://fem.um.es/Ejs/Google Scholar
Index Terms
- Solving AI problems with memristors: A case study for optimal
Recommendations
AFPTAS Results for Common Variants of Bin Packing: A New Method for Handling the Small Items
We consider two well-known natural variants of bin packing and show that these packing problems admit asymptotic fully polynomial time approximation schemes (AFPTASs). In bin packing problems, a set of one-dimensional items of size at most 1 is to be ...
Online algorithms for 1-space bounded multi dimensional bin packing and hypercube packing
In this paper, we study 1-space bounded multi-dimensional bin packing and hypercube packing. A sequence of items arrive over time, each item is a d -dimensional hyperbox (in bin packing) or hypercube (in hypercube packing), and the length of each side ...
The maximum resource bin packing problem
Usually, for bin packing problems, we try to minimize the number of bins used or in the case of the dual bin packing problem, maximize the number or total size of accepted items. This paper presents results for the opposite problems, where we would like ...
Comments