Abstract
In literature, different approaches point to the use of different resistive memory (RRAM) device families such as PCM [1], OxRAM, CBRAM [2], and STT-MRAM [3] for synaptic emulation in dedicated neuromorphic hardware. Most of these works justify the use of RRAM devices in hybrid learning hardware on grounds of their inherent advantages, such as ultra-high density, high endurance, high retention, CMOS compatibility, possibility of 3D integration, and low power consumption [4]. However, with the advent of more complex learning and weight update algorithms (beyond-STDP kinds), for example the ones inspired from Machine Learning, the peripheral synaptic circuit overhead considerably increases. Thus, use of RRAM cannot be justified on the merits of device properties alone. A more application-oriented approach is needed to further strengthen the case of RRAM devices in such systems that exploit the device properties also for peripheral nonsynaptic and learning circuitry, beyond the usual synaptic application alone.In this chapter, we discuss two novel designs utilizing the inherent variability in resistive memory devices to successfully implement modified versions of Extreme Learning Machines and Restricted Boltzmann Machines in hardware.
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
References
Suri, M., Bichler, O., Querlioz, D., Cueto, O., Perniola, L., Sousa, V., Vuillaume, D., Gamrat, C., DeSalvo, B.: Phase change memory as synapse for ultra-dense neuromorphic systems: Application to complex visual pattern extraction. 2011 IEEE International Electron Devices Meeting (IEDM), vol. 4, no. 4, pp. 5–7, Dec 2011
Suri, M., Querlioz, D., Bichler, O., Palma, G., Vianello, E., Vuillaume, D., Gamrat, C., DeSalvo, B.: Bio-inspired stochastic computing using binary cbram synapses. IEEE Trans. Electron Devices 60(7), 2402–2409 (2013)
Vincent, A.F., Larroque, J., Zhao, W.S., Romdhane, N.B. Bichler, O., Gamrat, C., Klein, J.O., Galdin-Retailleau, S., Querlioz, D.: Spin-transfer torque magnetic memory as a stochastic memristive synapse. In: Circuits and Systems (ISCAS), vol. 2014, pp. 1074–1077 (2014)
DeSalvo, B., Sousa, V., Perniola, L., Jahan, C., Maitrejean, S., Nodin, J., Cagli, C., Jousseaume, V., Molas, G., Vianello, E. et al.: Emerging memory technologies: Challenges and opportunities
Wong, H.-S.P., Lee, H.-Y., Yu, S., Chen, Y.-S., Wu, Y., Chen, P.S. Lee, B., Chen, F.T., Tsai, M.-J.: Metal–Oxide RRAM, vol. 100, no. 6. IEEE, pp. 1951–1970 (2012)
Gopalan, C., Ma, Y., Gallo, T., Wang, J., Runnion, E., Saenz, J., Koushan, F., Blanchard, P., Hollmer, S.: Demonstration of conductive bridging random access memory (cbram) in logic cmos process. Solid-State Electron. 58, 1 (2011)
Su, Y.-T., Chang, K.-C., Chang, T.-C., Tsai, T.-M., Zhang, R., Lou, J., Chen, J.-H., Young, T.-F., Chen, K.-H., Tseng, B.-H., et al.: Characteristics of hafnium oxide resistance random access memory with different setting compliance current. Appl. Phys. Lett. 103(16), 163502 (2013)
Yu, S., Guan, X., Wong, H.-S.P.: On the switching parameter variation of metal oxide rrampart ii: model corroboration and device design strategy. IEEE Trans. Electron Devices 59(4), 1183–1188 (2012)
Suri, M., Parmar, V., Sassine, G., Alibart, F.: Oxram based elm architecture for multi-class classification applications. Neural Netw. (IJCNN) 2015, 1–8 (2015)
Suri, M., Parmar, V.: Exploiting intrinsic variability of filamentary resistive memory for extreme learning machine architectures. IEEE Trans. Nanotechnol. 14(6), 963–968 (2015)
Raghavan, N.: Performance and Reliability Trade-offs for High-\(\kappa \) RRAM. Elsevier, vol. 54, no. 9 (2014)
Fantini, A., Goux, L., Degraeve, R., Wouters, D., Raghavan, N., Kar, G., Belmonte, A., Chen, Y.-Y., Govoreanu, B., Jurczak, M.: Intrinsic Switching Variability in Hfo 2 RRam, pp. 30–33 (2013)
Chen, A., Lin, M.-R.: Variability of resistive switching memories and its impact on crossbar array performance. Reliab. Phys. Symp. (IRPS) 2011 (2011)
Suri, M., Parmar, V., Kumar, A., Querlioz, D., Alibart, F.: Neuromorphic hybrid rram-cmos rbm architecture. In: 2015 15th Non-Volatile Memory Technology Symposium (NVMTS), pp. 1–6, Oct 2015
Huang, G.-B.: An insight into extreme learning machines: random neurons, random features and kernels. Cogn. Comput. 1–15 (2014)
Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: theory and applications. Neurocomputing 70(1), 489–501 (2006)
Yang, A.Y., Sastry, S.S., Ganesh, A., Ma, Y.: Fast l 1-minimization algorithms and an application in robust face recognition: A review, pp. 1849–1852 (2010)
Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: a new learning scheme of feedforward neural networks. Neural Netw. 2004, 985–990 (2004)
Merkel, C., Kudithipudi, D.: A current-mode cmos/memristor hybrid implementation of an extreme learning machine. In: Proceedings of the 24th edition of the Great Lakes Symposium on VLSI, pp. 241–242. ACM (2014)
Decherchi, S., Gastaldo, P., Leoncini, A., Zunino, R.: Efficient digital implementation of extreme learning machines for classification. IEEE Trans. Circuits Syst. II: Express Briefs 59(8), 496–500 (2012)
Lee, H.Y., Chen, P.S., Wu, T.Y., et al.: Low power and speed bipolar switching with a thin reactive buffer layer in robust HfO2 based RRAM. Int. Electron Devices Meet. (2008)
Su, Y.-T., et al.: Characteristics of hafnium oxide resistance random access memory with different setting compliance current. Appl. Phys. Lett. 103, 16 (2013)
Yu, S., Guan, X., Wong, H.-S.P.: On the stochastic nature of resistive switching in metal oxide rram: physical modeling, monte carlo simulation, and experimental characterization. Int. Electron Device Meet. (2011)
Shi, B., Chen, L., Lu, C.: Current controlled sigmoid neural circuit. U.S. Patent, vol. 6, Dec 2003
Irturk, A., Benson, B., Mirzaei, S., Kastner, R.: An fpga design space exploration tool for matrix inversion architectures. Appl. Specif. Processors 2008, 42–47 (2008)
Zhang, W., Betz, V., Rose, J.: Portable and scalable fpga-based acceleration of a direct linear system solver. In: ACM Transactions on Reconfigurable Technology and Systems (TRETS), vol. 5, p. 1 (2012)
Bache, K., Lichman, M.: UCI Machine Learning Repository [Irvine. CA: University of California, School of Information and Computer Science (2013). http://archive.ics.uci.edu/ml
Hinton, G.: A practical guide to training restricted boltzmann machines. Momentum 9(1), 926 (2010)
Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)
Pan, D., Wilamowski, B.M.: A vlsi implementation of mixed-signal mode bipolar neuron circuitry. In: Proceedings of the International Joint Conference on Neural Networks, 2003, vol. 2, pp. 971–976, July 2003
Li, H., Jiang, Z., Huang, P., Wu, Y., Chen, H.Y., Gao, B., Liu, X.Y., Kang, J.F., Wong, H.S.P.: Variation-aware, reliability-emphasized design and optimization of rram using spice model. In: 2015 Design, Automation Test in Europe Conference Exhibition (DATE), pp. 1425–1430, Mar 2015
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer (India) Pvt. Ltd.
About this chapter
Cite this chapter
Parmar, V., Suri, M. (2017). Exploiting Variability in Resistive Memory Devices for Cognitive Systems. In: Suri, M. (eds) Advances in Neuromorphic Hardware Exploiting Emerging Nanoscale Devices. Cognitive Systems Monographs, vol 31. Springer, New Delhi. https://doi.org/10.1007/978-81-322-3703-7_9
Download citation
DOI: https://doi.org/10.1007/978-81-322-3703-7_9
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-3701-3
Online ISBN: 978-81-322-3703-7
eBook Packages: EngineeringEngineering (R0)