Immunity to device variations in a spiking neural network with memristive nanodevices

IEEE Transactions on Nanotechnology

Volumn 12, Issue 3, 2013, Pages 288-295

  Querlioz, Damien ^a   Bichler, Olivier ^b   Dollfus, Philippe ^a   Gamrat, Christian ^b  

^a UNIVERSITÉ PARIS SACLAY   (France)

^b DIF   (France)

Author keywords

Memristive devices; memristors; neuromorphic; spike timing dependent plasticity (STPD); spiking neural networks; unsupervised learning

Indexed keywords

MEMRISTIVE DEVICES; NETWORK-BASED COMPUTING; NEUROMORPHIC; NON-VOLATILE MEMORY; SPIKE TIMING DEPENDENT PLASTICITIES; SPIKING NEURAL NETWORKS; SUPERVISED NETWORK; SYSTEM LEVEL SIMULATION;

CHARACTER RECOGNITION; COMPLEX NETWORKS; COMPUTER SIMULATION; INTELLIGENT AGENTS; MEMRISTORS; NANOSTRUCTURED MATERIALS; NETWORK PERFORMANCE; PHYSIOLOGY; UNSUPERVISED LEARNING;

NEURAL NETWORKS;

EID: 84877850976     PISSN: 1536125X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNANO.2013.2250995     Document Type: Article

References (49)

1. D. B. Strukov, G. S. Snider, D. R. Stewart, and R. S.Williams, "The missing memristor found," Nature, vol. 453, no. 7191, pp. 80-83, 2008. (Pubitemid 351630336)
2. F. Alibart, L. Gao, B. D. Hoskins, and D. B. Strukov, "High precision tuning of state for memristive devices by adaptable variation-tolerant algorithm," Nanotechnology, vol. 23, p. 075201, Feb. 2012.
3. G. Agnus, W. Zhao, V. Derycke, A. Filoramo, Y. Lhuillier, S. Lenfant, D. Vuillaume, C. Gamrat, and J.-P. Bourgoin, "Two-terminal carbon nanotube programmable devices for adaptive architectures," Adv. Mater., vol. 22, no. 6, pp. 702-706, 2010.
4. K.-H. Kim, S. Gaba, D. Wheeler, J. M. Cruz-Albrecht, T. Hussain, N. Srinivasa, and W. Lu, "A functional hybrid memristor crossbar- Array/CMOS system for data storage and neuromorphic applications," Nano Lett., vol. 12, no. 1, pp. 389-395, 2011.
5. E.Marder and J.-M. Goaillard, "Variability, compensation, and homeostasis in neuron and network function," Nat. Rev. Neurosci., vol. 7, no. 7, pp. 563-574, 2006. (Pubitemid 43995432)
6. J. V. Arthur and K. A.Boahen, "Learning in silicon: Timing is everything," Adv. Neural Inf. Process. Syst., vol. 18, pp. 281-1185, 2006.
7. G. Indiveri, E. Chicca, and R. Douglas, "A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity," IEEE Trans. Neural Netw., vol. 17, no. 1, pp. 211-221, Jan. 2006. (Pubitemid 43272460)
8. F. Alibart, S. Pleutin, D. Guérin, C. Novembre, S. Lenfant, K. Lmimouni, C. Gamrat, and D. Vuillaume, "An organic nanoparticle transistor behaving as a biological spiking synapse," Adv. Funct. Mater., vol. 20, no. 2, pp. 330-337, 2010.
9. J. Perez-Carrasco, C. Zamarreno-Ramos, T. Serrano-Gotarredona, and B. Linares-Barranco, "On neuromorphic spiking architectures for asynchronous STDP memristive systems," in Proc. IEEE Int. Symp. Circuits Syst., 2010, pp. 1659-1662.
10. G. Snider, "Spike-timing-dependent learning in memristive nanodevices," in Prof. IEEE Int. Symp. Nanoscale Archit (NANOARCH), 2008, pp. 85- 92.
11. A. Afifi, A. Ayatollahi, and F. Raissi, "Implementation of biologically plausible spiking neural network models on the memristor crossbar-based CMOS/nano circuits," in Proc. Eur. Conf. Circuit Theory Design, 2009, pp. 563-566.
12. D. Querlioz, O. Bichler, and C. Gamrat, "Simulation of a memristor-based spiking neural network immune to device variations," in Proc. Int. Joint Conf. Neural Netw., 2011, pp. 1775-1781.
13. S. H. Jo, T. Chang, I. Ebong, B. B. Bhadviya, P. Mazumder, and W. Lu, "Nanoscale memristor device as synapse in neuromorphic systems," Nano Lett., vol. 10, no. 4, pp. 1297-1301, 2010.
14. M. Suri, O. Bichler, D. Querlioz, O. Cueto, L. Perniola, V. Sousa, D. Vuillaume, C. Gamrat, and B. DeSalvo, "Phase change memory as synapse for ultra-dense neuromorphic systems: Application to complex visual pattern extraction," in Proc. IEEE Electron. Devices Meet. Tech. Dig., Dec. 2011, pp. 4.4.1-4.4.4.
15. K. Seo, I. Kim, S. Jung, M. Jo, S. Park, J. Park, J. Shin, K. P. Biju, J. Kong, K. Lee, B. Lee, and H. Hwang, "Analog memory and spiketiming- dependent plasticity characteristics of a nanoscale titanium oxide bilayer resistive switching device," Nanotechnology, vol. 22, p. 254023, Jun. 2011.
16. S. Yu, Y. Wu, R. Jeyasingh, D. Kuzum, and H. P. Wong, "An electronic synapse device based on metal oxide resistive switching memory for neuromorphic computation," IEEE Trans. Electron Devices, vol. 58, no. 8, pp. 2729-2737, Aug. 2011.
17. D. Kuzum, R. G. D. Jeyasingh, B. Lee, and H.-S. P. Wong, "Nanoelectronic programmable synapses based on phase change materials for braininspired computing," Nano Lett., vol. 12, no. 5, pp. 2179-2186, 2012.
18. O. Bichler, M. Suri, D. Querlioz, D. Vuillaume, B. DeSalvo, and C. Gamrat, "Visual pattern extraction using energy-efficient "2-PCM synapse" neuromorphic architecture," IEEE Trans. Electron Devices, vol. 59, no. 8, pp. 1-9, Aug. 2012.
19. H. Markram, J. Lubke, M. Frotscher, and B. Sakmann, "Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs," Science, vol. 275, pp. 2206-2214, Jan. 1997.
20. G.-Q. Bi and M.-M. Poo, "Synaptic modification by correlated activity: Hebb's postulate revisited," Annu. Rev. Neurosci., vol. 24, pp. 139-166, Mar. 2001. (Pubitemid 32695226)
21. Y. Dan and M.-M. Poo, "Spike timing-dependent plasticity of neural circuits," Neuron, vol. 44, pp. 23-30, Sep. 2004. (Pubitemid 39348828)
22. G. Snider, R. Amerson, D. Carter, H. Abdalla, M. S. Qureshi, J. Léveillée, M. Versace, H. Ames, S. Patrick, B. Chandler, A. Gorchetchnikov, and E. Mingolla, "From synapses to circuitry: Using memristive memory to explore the electronic brain," Computer, vol. 44, no. 2, pp. 21-28, 2011.
23. D. S. Modha, R. Ananthanarayanan, S. K. Esser, A. Ndirango, A. J. Sherbondy, and R. Singh, "Cognitive computing," Commun. ACM, vol. 54, pp. 62-71, Aug. 2011.
24. Y. V. Pershin and M. Di Ventra, "Neuromorphic, digital and quantum computation with memory circuit elements," Proc. IEEE, vol. 100, no. 6, pp. 2071-2080, Jun. 2012.
25. K. Cantley, A. Subramaniam, H. Stiegler, R. Chapman, and E. Vogel, "Hebbian learning in spiking neural networks with nanocrystalline silicon TFTs andmemristive synapses," IEEE Trans. Nanotechnol., vol. 10, no. 5, pp. 1066-1073, Sep. 2011.
26. J. Borghetti, G. S. Snider, P. J. Kuekes, J. J. Yang, D. R. Stewart, and R. S. Williams, "Memristive' switches enable 'stateful' logic operations via material implication," Nature, vol. 464, no. 7290, pp. 873-876, 2010.
27. S. Yu, X. Guan, and H.-S. Wong, "On the stochastic nature of resistive switching in metal oxide RRAM: Physical modeling, monte carlo simulation, and experimental characterization," in Proc. IEEE Int. Electron Devices Meet., Dec. 2011, pp. 17.3.1-17.3.4.
28. Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," in Proc. IEEE, Nov. 1998, vol. 86, no. 11, pp. 2278-2324.
29. J. R. Heath, P. J. Kuekes, G. S. Snider, and R. S. Williams, "A defecttolerant computer architecture: Opportunities for nanotechnology," Science, vol. 280, pp. 1716-1721, 1998. (Pubitemid 28283489)
30. J. H. Lee and K. K. Likharev, "Defect-tolerant nanoelectronic pattern classifiers," Int. J. Circuit Theory Appl., vol. 35, pp. 239-264, May 2007. (Pubitemid 46848192)
31. D. Chabi, W. Zhao, D. Querlioz, and J.-O. Klein, "Robust neural logick block (NLB) based on memristor crossbar array," in Proc. IEEE/ACMInt. Symp. Nanoscale Archit., 2011, pp. 137-143.
32. R. Guyonneau, R. VanRullen, and S. J. Thorpe, "Neurons tune to the earliest spikes through STDP," Neural Comput., vol. 17, no. 4, pp. 859- 879, 2005.
33. J. M. Brader, W. Senn, and S. Fusi, "Learning real-world stimuli in a neural network with spike-driven synaptic dynamics," Neural Comput., vol. 19, pp. 2881-2912, Nov. 2007.
34. B. Nessler, M. Pfeiffer, andW. Maass, "STDP enables spiking neurons to detect hidden causes of their inputs," in Proc. Adv. Neural Inf. Process. Syst., 2009, pp. 1357-1365.
35. S. Yu, B. Gao, Z. Fang, H. Y. Yu, J. F. Kang, and H. S. Wong, "A neuromorphic visual system using RRAM synaptic devices with sub-pJ energy and tolerance to variability: Experimental characterization and large-scale modeling," in Proc. IEEE Int. Electron Devices Meet. Tech. Dig., 2012, pp. 10.4-1-10.4-4.
36. G. S. Snider, "Self-organized computation with unreliable, memristive nanodevices," Nanotechnol., vol. 18, p. 365202, Sep. 2007.
37. G. Snider, "Instar and outstar learning with memristive nanodevices," Nanotechnology, vol. 22, p. 015201, Jan. 2011.
38. J. Liang and H. S. Wong, "Cross-point memory array without cell Selectors - Device characteristics and data storage pattern dependencies," IEEE Trans. Electron Devices, vol. 57, no. 10, pp. 2531-2538, Oct. 2010.
39. E. Linn, R. Rosezin, C. Kugeler, and R.Waser, "Complementary resistive switches for passive nanocrossbar memories," Nat. Mater., vol. 9, no. 5, pp. 403-406, 2010.
40. J. Joshua Yang, M. X. Zhang, M. D. Pickett, F. Miao, J. Paul Strachan, W.-D. Li, W. Yi, D. A. A. Ohlberg, B. Joon Choi, W. Wu, J. H. Nickel, G. Medeiros-Ribeiro, and R. S. Williams, "Engineering nonlinearity into memristors for passive crossbar applications," Appl. Phys. Lett., vol. 100, pp. 113501-1-113501-4, Mar. 2012.
41. M. Suri, O. Bichler, D. Querlioz, B. Traoŕe, O. Cueto, L. Perniola, V. Sousa, D. Vuillaume, C. Gamrat, and B. DeSalvo, "Physical aspects of low power synapses based on phase change memory devices," J. Appl. Phys., vol. 112, pp. 054904-1-054904-10, Sep. 2012.
42. P. Lichtsteiner, C. Posch, and T. Delbruck, "A 128x 128 120 dB 15 MUS latency asynchronous temporal contrast vision sensor," IEEE J. Solid- State Circuits, vol. 43, no. 2, pp. 566-576, Feb. 2008. (Pubitemid 351190224)
43. V. Chan, S.-C. Liu, and A. van Schaik, "AER EAR: A matched silicon cochlea pair with address event representation interface," IEEE Trans. Circuits Syst., Regul. Pap., vol. 54, no. 1, pp. 48-59, Jan. 2007. (Pubitemid 46351018)
44. D. Querlioz, P. Dollfus, O. Bichler, and C. Gamrat, "Learning with memristive devices:Howshouldwemodel their behavior?" in Proc. IEEE/ACM Int. Symp. Nanoscale Archit., 2011, p. 150.
45. P. Merolla, J. Arthur, F. Akopyan, N. Imam, R. Manohar, and D. Modha, "A digital neurosynaptic core using embedded crossbar memory with 45 pJ per spike in 45 nm," in Proc. IEEE Custom Integr. Circuits Conf., Sep. 2011, pp. 1-4.
46. C. Bartolozzi, O. Nikolayeva, andG. Indiveri, "Implementing homeostatic plasticity in VLSI networks of spiking neurons," in Proc. IEEE Int. Conf. Electron., Circuits Syst., 2008, pp. 682-685.
47. O. Bichler, D. Querlioz, S. J. Thorpe, J.-P. Bourgoin, and C. Gamrat, "Extraction of temporally correlated features from dynamic vision sensors with spike-timing-dependent plasticity," Neural Netw., vol. 32, pp. 339- 348, 2012.
48. D. Querlioz, W. S. Zhao, P.Dollfus, J.-O. Klein,O.Bichler, and C. Gamrat, "Bioinspired networks with nanoscale memristive devices that combine the unsupervised and supervised learning approaches," in Proc. IEEE Int. Symp. Nanoscale Archit., 2012, pp. 203-212.
49. D. C. Ciresan, U. Meier, L. M. Gambardella, and J. Schmidhuber, "Deep big simple neural nets excel on handwritten digit recognition," Neural Comput., vol. 22, no. 12, pp. 3207-3220, Mar. 2010.