Abstract
Technology advances in the last a few decades have resulted in profound changes in our society, from workplaces to living rooms to how we socialize with each other. These changes in turn drive further technology developments, as the exponential growth of data demands ever increasing computing power. However, improvements in computing capacity from device scaling alone is no longer sufficient, and new materials, devices, and architectures likely need to be developed collaboratively to meet present and future computing needs. Specifically, devices that offer co-located memory and computing characteristics, as represented by memristor devices and memristor-based computing systems, have attracted broad interest in the last decade. Besides tremendous appeal in data storage applications, memristors offer the potential for efficient hardware realization of neuromorphic computing architectures that can effectively address the memory and energy walls faced by conventional von Neumann computing architectures. In this review, we evaluate the state-of-the-art in memristor devices and systems, and highlight the potential and challenges of applying such devices and architectures in neuromorphic computing applications. New directions that can lead to general, efficient in-memory computing systems will also be discussed.
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References
Yang J J, Strukov D B, Stewart D R. Memristive devices for computing. Nat Nanotech, 2013, 8: 13–24
Kim K H, Gaba S, Wheeler D, et al. A functional hybrid memristor crossbar-array/CMOS system for data storage and neuromorphic applications. Nano Lett, 2012, 12: 389–395
Pershin Y V, Di Ventra M. Neuromorphic, digital, and quantum computation with memory circuit elements. Proc IEEE, 2012, 100: 2071–2080
Gaba S, Knag P, Zhang Z Y, et al. Memristive devices for stochastic computing. In: Proceedings of IEEE International Symposium on Circuits and Systems, Melbourne, 2014. 2592–2595
Zidan M, Jeong Y J, Shin J H, et al. Field-programmable crossbar array (FPCA) for reconfigurable computing. IEEE Trans Multi-Scale Comput Syst, 2017. doi: 10.1109/TMSCS.2017.2721160
Borghetti J, Snider G S, Kuekes P J, et al. ‘Memristive’ switches enable ‘stateful’ logic operations via material implication. Nature, 2010, 464: 873–876
Mead C. Neuromorphic electronic systems. Proc IEEE, 1990, 78: 1629–1636
Indiveri G, Horiuchi T K. Frontiers in neuromorphic engineering. Front Neurosci, 2011, 5: 118
Chicca E, Stefanini F, Bartolozzi C, et al. Neuromorphic electronic circuits for building autonomous cognitive systems. Proc IEEE, 2014, 102: 1367–1388
Gaba S, Sheridan P, Zhou J, et al. Stochastic memristive devices for computing and neuromorphic applications. Nanoscale, 2013, 5: 5872–5878
Prezioso M, Merrikh-Bayat F, Hoskins B D, et al. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature, 2015, 521: 61–64
Indiveri G, Linares-Barranco B, Legenstein R, et al. Integration of nanoscale memristor synapses in neuromorphic computing architectures. Nanotechnology, 2013, 24: 384010
Zidan M A, Chen A, Indiveri G, et al. Memristive computing devices and applications. J Electroceram, 2017, 39: 4–20
Chua L O, Kang S M. Memristive devices and systems. Proc IEEE, 1976, 64: 209–223
Strukov D B, Snider G S, Stewart D R, et al. The missing memristor found. Nature, 2008, 453: 80–83
Govoreanu B, Kar G S, Chen Y Y, et al. 10×10 nm2 Hf/HfOx crossbar resistive RAM with excellent performance, reliability and low-energy operation. In: Proceedings of IEEE International Electron Devices Meeting, Washington, 2011
Torrezan A C, Strachan J P, Medeiros-Ribeiro G, et al. Sub-nanosecond switching of a tantalum oxide memristor. Nanotechnology, 2011, 22: 485203
Lee M J, Lee C B, Lee D, et al. A fast, high-endurance and scalable non-volatile memory device made from asymmetric Ta2O5−x/TaO2−x bilayer structures. Nat Mater, 2011, 10: 625–630
Valov I, Lu W D. Nanoscale electrochemistry using dielectric thin films as solid electrolytes. Nanoscale, 2016, 8: 13828–13837
Younis A, Chu D, Lin X, et al. High-performance nanocomposite based memristor with controlled quantum dots as charge traps. ACS Appl Mater Interface, 2013, 5: 2249–2254
Stoliar P, Rozenberg M, Janod E, et al. Nonthermal and purely electronic resistive switching in a Mott memory. Phys Rev B, 2014, 90: 45146
Wong H S P, Raoux S, Kim S B, et al. Phase change memory. Proc IEEE, 2010, 98: 2201–2227
Diao Z T, Li Z J, Wang S Y, et al. Spin-transfer torque switching in magnetic tunnel junctions and spin-transfer torque random access memory. J Phys-Condens Matter, 2007, 19: 165209
Sheridan P M, Cai F X, Du C, et al. Sparse coding with memristor networks. Nat Nanotech, 2017, 12: 784–789
Chang T, Jo S H, Kim K H, et al. Synaptic behaviors and modeling of a metal oxide memristive device. Appl Phys A, 2011, 102: 857–863
Hasegawa T, Ohno T, Terabe K, et al. Learning abilities achieved by a single solid-state atomic switch. Adv Mater, 2010, 22: 1831–1834
Jo S H, Chang T, Ebong I, et al. Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett, 2010, 10: 1297–1301
Kim S, Choi S H, Lu W. Comprehensive physical model of dynamic resistive switching in an oxide memristor. ACS Nano, 2014, 8: 2369–2376
Seo K, Kim I, Jung S, et al. Analog memory and spike-timing-dependent plasticity characteristics of a nanoscale titanium oxide bilayer resistive switching device. Nanotechnology, 2011, 22: 254023
Kim S, Du C, Sheridan P, et al. Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity. Nano Lett, 2015, 15: 2203–2211
Du C, Ma W, Chang T, et al. Biorealistic implementation of synaptic functions with oxide memristors through internal ionic dynamics. Adv Funct Mater, 2015, 25: 4290–4299
Kuzum D, Yu S, Wong H S. Synaptic electronics: materials, devices and applications. Nanotechnology, 2013, 24: 382001
Wang Z R, Joshi S, Savelev S E, et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat Mater, 2017, 16: 101–108
Zidan M A, Jeong Y J, Lu W D. Temporal learning using second-order memristors. IEEE Trans Nanotechnol, 2017, 16: 721–723
Ma W, Chen L, Du C, et al. Temporal information encoding in dynamic memristive devices. Appl Phys Lett, 2015, 107: 193101
Zhu X, Du C, Jeong Y J, et al. Emulation of synaptic metaplasticity in memristors. Nanoscale, 2017, 9: 45–51
Yang Y, Chen B, Lu W D. Memristive physically evolving networks enabling the emulation of heterosynaptic plasticity. Adv Mater, 2015, 27: 7720–7727
Merolla P A, Arthur J V, Alvarez-Icaza R, et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, 2014, 345: 668–673
Benjamin B V, Gao P, McQuinn E, et al. Neurogrid: a mixed-analog-digital multichip system for large-scale neural simulations. Proc IEEE, 2014, 102: 699–716
Furber S B, Galluppi F, Temple S, et al. The SpiNNaker project. Proc IEEE, 2014, 102: 652–665
Schemmel J, Briiderle D, Griibl A, et al. A wafer-scale neuromorphic hardware system for large-scale neural modeling. In: Proceedings of IEEE International Symposium on Circuits and Systems, Paris, 2010. 1947–1950
Pfeil T, Grübl A, Jeltsch S, et al. Six networks on a universal neuromorphic computing substrate. Front Neurosci, 2013, 7: 11
Indiveri G, Liu S C. Memory and information processing in neuromorphic systems. Proc IEEE, 2015, 103: 1379–1397
Alibart F, Zamanidoost E, Strukov D B. Pattern classification by memristive crossbar circuits using ex situ and in situ training. Nat Commun, 2013, 4: 2072
Sheridan P M, Du C, Lu W D. Feature extraction using memristor networks. IEEE Trans Neural Netw Learning Syst, 2016, 27: 2327–2336
Choi S, Sheridan P, Lu W D. Data clustering using memristor networks. Sci Rep, 2015, 5: 10492
Sheridan P, Ma W, Lu W. Pattern recognition with memristor networks. In: Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS), Melbourne, 2014. 1078–1081
Adhikari S P, Yang C J, Kim H, et al. Memristor bridge synapse-based neural network and its learning. IEEE Trans Neural Netw Learning Syst, 2012, 23: 1426–1435
Hu M, Strachan J P, Grafals E M, et al. Dot-product engine for neuromorphic computing. In: Proceedings of the 53rd Annual Design Automation Conference, Austin, 2016
Choi S, Shin J H, Lee J, et al. Experimental demonstration of feature extraction and dimensionality reduction using memristor networks. Nano Lett, 2017, 17: 3113–3118
Yu S, Chen P Y, Cao Y, et al. Scaling-up resistive synaptic arrays for neuro-inspired architecture: challenges and prospect. In: Proceedings of International Electron Devices Meeting, Washington, 2015
Sheridan P, Lu W D. Defect consideratons for robust sparse coding using memristor arrays. In: Proceedings of the 2015 IEEE/ACM International Symposium on Nanoscale Architectures, Boston, 2015, 137–138
Ma W, Cai F, Du C, et al. Device nonideality effects on image reconstruction using memristor arrays. In: Proceedings of 2016 IEEE International Electron Devices Meeting (IEDM), San Francisco, 2016
Kumar S, Strachan J P, Williams R S. Chaotic dynamics in nanoscale NbO2 Mott memristors for analogue computing. Nature, 2017, 548: 318–321
Tuma T, Pantazi A, Le Gallo M, et al. Stochastic phase-change neurons. Nat Nanotech, 2016, 11: 693–699
Chen B, Cai F X, Zhou J T, et al. Efficient in-memory computing architecture based on crossbar arrays. In: Proceedings of International Electron Devices Meeting, Washington, 2015
Acknowledgements
This work was supported in part by National Science Foundation (NSF) (Grant Nos. ECCS-1708700, CCF-1617315). We would like to thank F CAI, J LEE and J SHIN for helpful discussions.
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Ma, W., Zidan, M.A. & Lu, W.D. Neuromorphic computing with memristive devices. Sci. China Inf. Sci. 61, 060422 (2018). https://doi.org/10.1007/s11432-017-9424-y
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DOI: https://doi.org/10.1007/s11432-017-9424-y