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Computational capacity and energy consumption of complex resistive switch networks

1 Department of Electrical and Computer Engineering, Portland State University, Portland, OR, 97211, USA;
2 Department of Computer Science, University of New Mexico, Albuquerque, NM, 87131, USA

Special Issues: Nanomaterials for Cognitive Technology

Resistive switches are a class of emerging nanoelectronics devices that exhibit a wide variety of switching characteristics closely resembling behaviors of biological synapses. Assembled into random networks, such resistive switches produce emerging behaviors far more complex than that of individual devices. This was previously demonstrated in simulations that exploit information processing within these random networks to solve tasks that require nonlinear computation as well as memory. Physical assemblies of such networks manifest complex spatial structures and basic processing capabilities often related to biologically-inspired computing. We model and simulate random resistive switch networks and analyze their computational capacities. We provide a detailed discussion of the relevant design parameters and establish the link to the physical assemblies by relating the modeling parameters to physical parameters. More globally connected networks and an increased network switching activity are means to increase the computational capacity linearly at the expense of exponentially growing energy consumption. We discuss a new modular approach that exhibits higher computational capacities, and energy consumption growing linearly with the number of networks used. The results show how to optimize the trade-o between computational capacity and energy e ciency and are relevant for the design and fabrication of novel computing architectures that harness random assemblies of emerging nanodevices.
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Keywords memrisive networks; atomic switch networks; network model; reservoir computing

Citation: Jens Bürger, Alireza Goudarzi, Darko Stefanovic, Christof Teuscher. Computational capacity and energy consumption of complex resistive switch networks. AIMS Materials Science, 2015, 2(4): 530-545. doi: 10.3934/matersci.2015.4.530


  • 1. Chang T, Yang Y, LuW(2013) Building Neuromorphic Circuits with Memristive Devices. Circuits, Systems Magazine, IEEE 13: 56-73.
  • 2. Crutchfield JP, Ditto WL, Sinha S (2010) Introduction to focus issue: intrinsic, designed computation: information processing in dynamical systems-beyond the digital hegemony. Chaos: An Interdisciplinary Journal of Nonlinear Science 20: 037101.    
  • 3. Hasegawa T, Ohno T, Terabe K, et al. (2010) Learning Abilities Achieved by a Single Solid-State Atomic Switch. Adv Mater 22: 1831-1834.    
  • 4. Avizienis AV, Sillin HO, Martin-Olmos C, et al. (2012) Neuromorphic atomic switch networks. PLoS ONE 7: e42772.    
  • 5. Sillin HO, Aguilera R, Shieh HH, et al. (2013) A theoretical, experimental study of neuromorphic atomic switch networks for reservoir computing. Nanotechnology 24: 384004.    
  • 6. Stieg AZ, Avizienis AV, Sillin HO, et al. (2012) Emergent Criticality in Complex Turing B-Type Atomic Switch Networks. Adv Mater 24: 286-293.    
  • 7. Demis EC, Aguilera R, Sillin HO, et al. (2015) Atomic switch networks - nanoarchitectonic design of a complex system for natural computing. Nanotechnology 26: 204003.    
  • 8. Sporns O (2011) Networks of the Brain. The MIT Press, Cambridge, MA.
  • 9. Chua L (1971) Memristor - The missing circuit element. Circuit Theory, IEEE Transactions on 18: 507-519.    
  • 10. Strukov DB, Snider GS, Stewart DR, et al. (2008) The missing memristor found. Nature 453: 80-83.    
  • 11. Jo SH, Chang T, Ebong I, et al. (2010) Nanoscale Memristor Device as Synapse in Neuromorphic Systems. Nano Lett 10: 1297-1301.    
  • 12. Kim S, Du C, Sheridan P, et al. (2015) Experimental Demonstration of a Second-Order Memristor, Its Ability to Biorealistically Implement Synaptic Plasticity. Nano Lett 15: 2203-2211.    
  • 13. Ohno T, Hasegawa T, Tsuruoka T, et al. (2011) Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. Nat Mater 10: 591-595.    
  • 14. Sah MP, Chua LO (2014) Brains Are Made of Memristors. IEEE Circuits, Systems Magazine 14: 12-36.
  • 15. Bürger J, Teuscher C (2013) Variation-tolerant Computing with Memristive Reservoirs. Nanoscale Architectures (NANOARCH), 2013 IEEE/ACM International Symposium on, 1-6.
  • 16. Bürger J, Goudarzi A, Stefanovic D, et al. (2015) Hierarchical Composition of Memristive Networks for Real-Time Computing. Nanoscale Architectures (NANOARCH), 2015 IEEE/ACM International Symposium on, 33-38.
  • 17. Kulkarni MS, Teuscher C (2012) Memristor-based Reservoir Computing. Nanoscale Architectures (NANOARCH), 2012 IEEE/ACM International Symposium on, 226-232.
  • 18. Jaeger H (2001) The “echo state” approach to analysing, training recurrent neural networks - with an Erratum note. GMD Report 148, German National Research Center for Information Technology.
  • 19. Maass W, Natschläger T, Markram H (2002) Real-time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations. Neural Comput 14: 2531-2560.    
  • 20. Hasegawa T, Nayak A, Ohno T, et al. (2011) Memristive operations demonstrated by gap-type atomic switches. Appl Phys A 102: 811-815.    
  • 21. Chang T, Jo SH, Lu W (2011) Short-term memory to long-term memory transition in a nanoscale memristor. ACS Nano 5: 7669-76.    
  • 22. Strukov DB, Williams RS (2009) Exponential ionic drift: fast switching and low volatility of thin-film memristors. Appl Phys A 94: 515-519.    
  • 23. Tamura T, Hasegawa T, Terabe K, et al. (2006) Switching Property of Atomic Switch Controlled by Solid Electrochemical Reaction. Jpn J Appl Phys 45: L364-L366.    
  • 24. Chang T, Jo SH, Kim KH, et al. (2011) Synaptic behaviors, modeling of a metal oxide memristive device. Appl Phys A 102: 857-863.
  • 25. Gaba S, Sheridan P, Zhou J, et al. (2013) Stochastic memristive devices for computing, neuromorphic applications. Nanoscale 5: 5872-5878.    
  • 26. Ohno T, Hasegawa T, Nayak A, et al. (2011) Sensory, short-term memory formations observed in a Ag2S gap-type atomic switch. Appl Phys Lett 99: 203108.    
  • 27. Stieg AZ, Avizienis AV, Sillin HO, et al. (2014) Self-organized atomic switch networks. Jpn J Appl Phys 53: 0-6.
  • 28. Litovski V, Zwolinski M (1997) VLSI Circuit Simulation and Optimization. Chapman & Hall, London, UK.
  • 29. Rabinovich M, Huerta R, Laurent G (2008) Transient Dynamics for Neural Processing. Science 321: 48-50.    
  • 30. Buonomano DV, Maass W (2009) State-dependent computations: spatiotemporal processing in cortical networks. Nat Rev Neurosci 10: 113-125.    
  • 31. Bertschinger N, Natschläger T (2004) Real-time computation at the edge of chaos in recurrent neural networks. Neural Comput 16: 1413-1436.    
  • 32. Langton CG (1990) Computation at the edge of chaos: Phase transitions and emergent computation. Physica D: Nonlinear Phenomena 42: 12-37.    
  • 33. Snyder D, Goudarzi A, Teuscher C (2013) Computational capabilities of random automata networks for reservoir computing. Phys Rev E 87: 042808.    
  • 34. Bishop CM (2006) Pattern Recognition, Machine Learning (Information Science, Statistics). Springer-Verlag New York, Inc., Secaucus, NJ, USA.
  • 35. Honey CJ, Thivierge JP, Sporns O (2010) Can structure predict function in the human brain? NeuroImage 52: 766-776.    
  • 36. Shah MM, Hammond RS, Hoffman DA (2010) Dendritic ion channel trafficking , plasticity. Trends Neurosci 33: 307-316.    


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Copyright Info: 2015, Jens Bürger, Alireza Goudarzi, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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