Abstract
AbstractNeuromorphic computing has emerged as a promising avenue towards building the next generation of intelligent computing systems. It has been proposed that memristive devices, which exhibit history-dependent conductivity modulation, could efficiently represent the synaptic weights in artificial neural networks. However, precise modulation of the device conductance over a wide dynamic range, necessary to maintain high network accuracy, is proving to be challenging. To address this, we present a multi-memristive synaptic architecture with an efficient global counter-based arbitration scheme. We focus on phase change memory devices, develop a comprehensive model and demonstrate via simulations the effectiveness of the concept for both spiking and non-spiking neural networks. Moreover, we present experimental results involving over a million phase change memory devices for unsupervised learning of temporal correlations using a spiking neural network. The work presents a significant step towards the realization of large-scale and energy-efficient neuromorphic computing systems.
Authors
10
- Irem Boybat (first)
- Manuel Le Gallo (additional)
- S. R. Nandakumar (additional)
- Timoleon Moraitis (additional)
- Thomas Parnell (additional)
- Tomas Tuma (additional)
- Bipin Rajendran (additional)
- Yusuf Leblebici (additional)
- Abu Sebastian (additional)
- Evangelos Eleftheriou (additional)
References
64
Referenced
691
-
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
(
10.1038/nature14539
) / Nature by Y LeCun (2015) -
Schemmel, J. et al. A wafer-scale neuromorphic hardware system for large-scale neural modeling. In Proc. IEEE International Symposium on Circuits and Systems (ISCAS), 1947–1950 (IEEE, Paris, France, 2010).
(
10.1109/ISCAS.2010.5536970
) -
Painkras, E. et al. SpiNNaker: a 1-W 18-core system-on-chip for massively-parallel neural network simulation. IEEE J. Solid-State Circuits 48, 1943–1953 (2013).
(
10.1109/JSSC.2013.2259038
) / IEEE J. Solid-State Circuits by E Painkras (2013) -
Merolla, P. A. et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345, 668–673 (2014).
(
10.1126/science.1254642
) / Science by PA Merolla (2014) -
Qiao, N. et al. A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses. Front. Neurosci. 9, 141 (2015).
(
10.3389/fnins.2015.00141
) / Front. Neurosci. by N Qiao (2015) -
Beck, A., Bednorz, J., Gerber, C., Rossel, C. & Widmer, D. Reproducible switching effect in thin oxide films for memory applications. Appl. Phys. Lett. 77, 139–141 (2000).
(
10.1063/1.126902
) / Appl. Phys. Lett. by A Beck (2000) -
Strukov, D. B., Snider, G. S., Stewart, D. R. & Williams, R. S. The missing memristor found. Nature 453, 80–83 (2008).
(
10.1038/nature06932
) / Nature by DB Strukov (2008) -
Chua, L. Resistance switching memories are memristors. Appl. Phys. A 102, 765–783 (2011).
(
10.1007/s00339-011-6264-9
) / Appl. Phys. A by L Chua (2011) -
Wong, H. S. P. & Salahuddin, S. Memory leads the way to better computing. Nat. Nanotech. 10, 191–194 (2015).
(
10.1038/nnano.2015.29
) / Nat. Nanotech. by HSP Wong (2015) - Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. In Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1: Foundations, (eds Rumelhart, D. E. & McClelland, J. L.) 318–362 (MIT Press, Cambridge, MA, 1986).
-
Markram, H., Lübke, J., Frotscher, M. & Sakmann, B. Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science 275, 213–215 (1997).
(
10.1126/science.275.5297.213
) / Science by H Markram (1997) -
Anwani, N. & Rajendran, B. NormAD - normalized approximate descent based supervised learning rule for spiking neurons. In Proc. International Joint Conference on Neural Networks (IJCNN), 1–8 (IEEE, Killarney, Ireland, 2015).
(
10.1109/IJCNN.2015.7280618
) -
Saighi, S. et al. Plasticity in memristive devices for spiking neural networks. Front. Neurosci. 9, 51 (2015).
(
10.3389/fnins.2015.00051
) / Front. Neurosci. by S Saighi (2015) - Burr, G. W. et al. Integration of nanoscale memristor synapses in neuromorphic computing architectures. Adv. Phys. X 2, 89–124 (2016). / Adv. Phys. X by GW Burr (2016)
-
Rajendran, B. & Alibart, F. Neuromorphic computing based on emerging memory technologies. IEEE J. Emerg. Sel. Top. Circuits Syst. 6, 198–211 (2016).
(
10.1109/JETCAS.2016.2533298
) / IEEE J. Emerg. Sel. Top. Circuits Syst. by B Rajendran (2016) -
Ohno, T. et al. Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. Nat. Mater. 10, 591–595 (2011).
(
10.1038/nmat3054
) / Nat. Mater. by T Ohno (2011) -
Yu, S., Wu, Y., Jeyasingh, R., Kuzum, D. & Wong, H. S. P. An electronic synapse device based on metal oxide resistive switching memory for neuromorphic computation. IEEE Trans. Electron Dev. 58, 2729–2737 (2011).
(
10.1109/TED.2011.2147791
) / IEEE Trans. Electron Dev. by S Yu (2011) -
Ambrogio, S. et al. Neuromorphic learning and recognition with one-transistor-one-resistor synapses and bistable metal oxide RRAM. IEEE Trans. Electron Dev. 63, 1508–1515 (2016).
(
10.1109/TED.2016.2526647
) / IEEE Trans. Electron Dev. by S Ambrogio (2016) -
Covi, E. et al. Analog memristive synapse in spiking networks implementing unsupervised learning. Front. Neurosci. 10, 482 (2016).
(
10.3389/fnins.2016.00482
) / Front. Neurosci. by E Covi (2016) -
van de Burgt, Y. et al. A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. Nat. Mater. 16, 414–418 (2017).
(
10.1038/nmat4856
) / Nat. Mater. by Y van de Burgt (2017) -
Wang, Z. et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat. Mater. 16, 101–108 (2017).
(
10.1038/nmat4756
) / Nat. Mater. by Z Wang (2017) -
Boyn, S. et al. Learning through ferroelectric domain dynamics in solid-state synapses. Nat. Commun. 8, 14736 (2017).
(
10.1038/ncomms14736
) / Nat. Commun. by S Boyn (2017) -
Wu, W., Zhu, X., Kang, S., Yuen, K. & Gilmore, R. Probabilistically programmed STT-MRAM. IEEE J. Emerg. Sel. Top. Circuits Syst. 2, 42–51 (2012).
(
10.1109/JETCAS.2012.2187401
) / IEEE J. Emerg. Sel. Top. Circuits Syst. by W Wu (2012) -
Vincent, A. F. et al. Spin-transfer torque magnetic memory as a stochastic memristive synapse for neuromorphic systems. IEEE Trans. Biomed. Circ. Syst. 9, 166–174 (2015).
(
10.1109/TBCAS.2015.2414423
) / IEEE Trans. Biomed. Circ. Syst. by AF Vincent (2015) -
Kuzum, D., Jeyasingh, R. G. D., Lee, B. & Wong, H. S. P. Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing. Nano Lett. 12, 2179–2186 (2012).
(
10.1021/nl201040y
) / Nano Lett. by D Kuzum (2012) -
Ambrogio, S. et al. Unsupervised learning by spike timing dependent plasticity in phase change memory (PCM) synapses. Front. Neurosci. 10, 56 (2016).
(
10.3389/fnins.2016.00056
) / Front. Neurosci. by S Ambrogio (2016) -
Alibart, F., Zamanidoost, E. & Strukov, D. B. Pattern classification by memristive crossbar circuits using ex situ and in situ training. Nat. Commun. 4, 2072 (2013).
(
10.1038/ncomms3072
) / Nat. Commun. by F Alibart (2013) -
Indiveri, G., Linares-Barranco, B., Legenstein, R., Deligeorgis, G. & Prodromakis, T. Integration of nanoscale memristor synapses in neuromorphic computing architectures. Nanotechnology 24, 384010 (2013).
(
10.1088/0957-4484/24/38/384010
) / Nanotechnology by G Indiveri (2013) -
Prezioso, M. et al. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature 521, 61–64 (2015).
(
10.1038/nature14441
) / Nature by M Prezioso (2015) -
Kim, S. et al. NVM neuromorphic core with 64k-cell (256-by-256) phase change memory synaptic array with on-chip neuron circuits for continuous in-situ learning. In Proc. IEEE International Electron Devices Meeting (IEDM), 17-1 (IEEE, Washington, DC, USA, 2015).
(
10.1109/IEDM.2015.7409716
) -
Mostafa, H. et al. Implementation of a spike-based perceptron learning rule using TiO2−x memristors. Front. Neurosci. 9, 357 (2015).
(
10.3389/fnins.2015.00357
) / Front. Neurosci. by H Mostafa (2015) -
Tuma, T., Le Gallo, M., Sebastian, A. & Eleftheriou, E. Detecting correlations using phase-change neurons and synapses. IEEE Electron Dev. Lett. 37, 1238–1241 (2016).
(
10.1109/LED.2016.2591181
) / IEEE Electron Dev. Lett. by T Tuma (2016) -
Wozniak, S., Tuma, T., Pantazi, A. & Eleftheriou, E. Learning spatio-temporal patterns in the presence of input noise using phase-change memristors. In Proc. IEEE International Symposium on Circuits and Systems (ISCAS), 365–368 (IEEE, Montreal, QC, Canada, 2016).
(
10.1109/ISCAS.2016.7527246
) -
Burr, G. W. et al. Experimental demonstration and tolerancing of a large-scale neural network (165 000 synapses) using phase-change memory as the synaptic weight element. IEEE Trans. Electron Dev. 62, 3498–3507 (2015).
(
10.1109/TED.2015.2439635
) / IEEE Trans. Electron Dev. by GW Burr (2015) -
Koelmans, W. W. et al. Projected phase-change memory devices. Nat. Commun. 6, 8181 (2015).
(
10.1038/ncomms9181
) / Nat. Commun. by WW Koelmans (2015) -
Fuller, E. J. et al. Li-ion synaptic transistor for low power analog computing. Adv. Mater. 29, 1604310 (2017).
(
10.1002/adma.201604310
) / Adv. Mater by EJ Fuller (2017) -
Suri, M. et al. Phase change memory as synapse for ultra-dense neuromorphic systems: Application to complex visual pattern extraction. In Proc. IEEE International Electron Devices Meeting (IEDM), 4.4.1–4.4.4 (IEEE, Washington, DC, USA, 2011).
(
10.1109/IEDM.2011.6131488
) - Bill, J. & Legenstein, R. A compound memristive synapse model for statistical learning through STDP in spiking neural networks. Front. Neurosci. 8, 412 (2014). / Front. Neurosci. by J Bill (2014)
-
Garbin, D. et al. HfO2-based OxRAM devices as synapses for convolutional neural networks. IEEE Trans. Electron Dev. 62, 2494–2501 (2015).
(
10.1109/TED.2015.2440102
) / IEEE Trans. Electron Dev. by D Garbin (2015) -
Burr, G. W. et al. Recent progress in phase-change memory technology. IEEE J. Emerg. Sel. Top. Circuits Syst. 6, 146–162 (2016).
(
10.1109/JETCAS.2016.2547718
) / IEEE J. Emerg. Sel. Top. Circuits Syst. by GW Burr (2016) -
Sebastian, A., Le Gallo, M. & Krebs, D. Crystal growth within a phase change memory cell. Nat. Commun. 5, 4314 (2014).
(
10.1038/ncomms5314
) / Nat. Commun. by A Sebastian (2014) -
Close, G. F. et al. Device, circuit and system-level analysis of noise in multi-bit phase-change memory. In Proc. IEEE Int. Electron Devices Meeting (IEDM), 29.5.1–29.5.4 (IEEE, San Francisco, CA, USA, 2010).
(
10.1109/IEDM.2010.5703445
) -
Boybat, I. et al. Stochastic weight updates in phase-change memory-based synapses and their influence on artificial neural networks. In Proc. Ph.D. Research in Microelectronics and Electronics (PRIME), 13–16 (IEEE, Giardini Naxos, Italy, 2017).
(
10.1109/PRIME.2017.7974095
) -
Gokmen, T. & Vlasov, Y. Acceleration of deep neural network training with resistive cross-point devices: design considerations. Front. Neurosci. 10, 333 (2016).
(
10.3389/fnins.2016.00333
) / Front. Neurosci. by T Gokmen (2016) -
Nandakumar, S. R. et al. Supervised learning in spiking neural networks with MLC PCM synapses. In Proc. Device Research Conference (DRC), 1–2 (IEEE, South Bend, IN, USA, 2017).
(
10.1109/DRC.2017.7999481
) -
Le Gallo, M., Tuma, T., Zipoli, F., Sebastian, A. & Eleftheriou, E. Inherent stochasticity in phase-change memory devices. In Proc. European Solid-State Device Research Conference (ESSDERC), 373–376 (IEEE, Lausanne, Switzerland, 2016).
(
10.1109/ESSDERC.2016.7599664
) -
LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998).
(
10.1109/5.726791
) / Proc. IEEE by Y LeCun (1998) -
Querlioz, D., Bichler, O. & Gamrat, C. Simulation of a memristor-based spiking neural network immune to device variations. In Proc. International Joint Conference on Neural Networks (IJCNN), 1775–1781 (IEEE, San Jose, CA, USA, 2011).
(
10.1109/IJCNN.2011.6033439
) -
Gütig, R., Aharonov, R., Rotter, S. & Sompolinsky, H. Learning input correlations through nonlinear temporally asymmetric hebbian plasticity. J. Neurosci. 23, 3697–3714 (2003).
(
10.1523/JNEUROSCI.23-09-03697.2003
) / J. Neurosci. by R Gütig (2003) -
Tuma, T., Pantazi, A., Le Gallo, M., Sebastian, A. & Eleftheriou, E. Stochastic phase-change neurons. Nat. Nanotech. 11, 693–699 (2016).
(
10.1038/nnano.2016.70
) / Nat. Nanotech. by T Tuma (2016) -
Sebastian, A. et al. Temporal correlation detection using computational phase-change memory. Nat. Commun. 8, 1115 (2017).
(
10.1038/s41467-017-01481-9
) / Nat. Commun. by A Sebastian (2017) -
Edwards, F. LTP is a long term problem. Nature 350, 271–272 (1991).
(
10.1038/350271a0
) / Nature by F Edwards (1991) -
Bolshakov, V. Y., Golan, H., Kandel, E. R. & Siegelbaum, S. A. Recruitment of new sites of synaptic transmission during the cAMP-dependent late phase of LTP at CA3-CA1 synapses in the hippocampus. Neuron 19, 635–651 (1997).
(
10.1016/S0896-6273(00)80377-3
) / Neuron by VY Bolshakov (1997) -
Malenka, R. C. & Nicoll, R. A. Long-term potentiation-a decade of progress? Science 285, 1870–1874 (1999).
(
10.1126/science.285.5435.1870
) / Science by RC Malenka (1999) -
Malenka, R. C. & Bear, M. F. LTP and LTD: an embarrassment of riches. Neuron 44, 5–21 (2004).
(
10.1016/j.neuron.2004.09.012
) / Neuron by RC Malenka (2004) -
Benke, T. A., Luthi, A., Isaac, J. T. R. & Collingridge, G. L. Modulation of AMPA receptor unitary conductance by synaptic activity. Nature 393, 793–797 (1998).
(
10.1038/31709
) / Nature by TA Benke (1998) -
Le Gallo, M., Sebastian, A., Cherubini, G., Giefers, H. & Eleftheriou, E. Compressed sensing recovery using computational memory. In Proc. IEEE International Electron Devices Meeting (IEDM), 28.3.1–28.3.4 (IEEE, San Francisco, CA, USA, 2017).
(
10.1109/IEDM.2017.8268469
) -
Li, C. et al. Analogue signal and image processing with large memristor crossbars. Nat. Electron. 1, 52–59 (2018).
(
10.1038/s41928-017-0002-z
) / Nat. Electron. by C Li (2018) -
Le Gallo, M. et al. Mixed-precision in-memory computing. Nat. Electron. 1, 246–253 (2018).
(
10.1038/s41928-018-0054-8
) / Nat. Electron. by M Le Gallo (2018) -
Eryilmaz, S. B. et al. Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array. Front. Neurosci. 8, 205 (2014).
(
10.3389/fnins.2014.00205
) / Front. Neurosci. by SB Eryilmaz (2014) -
Sidler, S. et al. Large-scale neural networks implemented with non-volatile memory as the synaptic weight element: Impact of conductance response. In Proc. European Solid-State Device Research Conference (ESSDERC), 440–443 (IEEE, Lausanne, Switzerland, 2016).
(
10.1109/ESSDERC.2016.7599680
) -
Fumarola, A. et al. Accelerating machine learning with non-volatile memory: exploring device and circuit tradeoffs. In Proc. IEEE International Conference on Rebooting Computing (ICRC), 1–8 (IEEE, San Diego, CA, USA, 2016).
(
10.1109/ICRC.2016.7738684
) -
Sebastian, A., Krebs, D., Le Gallo, M., Pozidis, H. & Eleftheriou, E. A collective relaxation model for resistance drift in phase change memory cells. In Proc. IEEE International Reliability Physics Symposium (IRPS), MY-5 (IEEE, Monterey, CA, USA, 2015).
(
10.1109/IRPS.2015.7112808
) -
Song, S., Miller, K. D. & F., A. L. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat. Neurosci. 3, 919–926 (2000).
(
10.1038/78829
) / Nat. Neurosci. by S Song (2000)
Dates
Type | When |
---|---|
Created | 7 years, 2 months ago (June 22, 2018, 6:15 a.m.) |
Deposited | 1 month, 3 weeks ago (July 5, 2025, 3:52 a.m.) |
Indexed | 2 hours, 52 minutes ago (Aug. 28, 2025, 8:08 a.m.) |
Issued | 7 years, 2 months ago (June 28, 2018) |
Published | 7 years, 2 months ago (June 28, 2018) |
Published Online | 7 years, 2 months ago (June 28, 2018) |
@article{Boybat_2018, title={Neuromorphic computing with multi-memristive synapses}, volume={9}, ISSN={2041-1723}, url={http://dx.doi.org/10.1038/s41467-018-04933-y}, DOI={10.1038/s41467-018-04933-y}, number={1}, journal={Nature Communications}, publisher={Springer Science and Business Media LLC}, author={Boybat, Irem and Le Gallo, Manuel and Nandakumar, S. R. and Moraitis, Timoleon and Parnell, Thomas and Tuma, Tomas and Rajendran, Bipin and Leblebici, Yusuf and Sebastian, Abu and Eleftheriou, Evangelos}, year={2018}, month=jun }