The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Immunity to Device Variations in a Spiking Neural Network With Memristive Nanodevices
Open accessAbstract
Memristive nanodevices can feature a compact multilevel nonvolatile memory function, but are prone to device variability. We propose a novel neural network-based computing paradigm, which exploits their specific physics, and which has virtual immunity to their variability. Memristive devices are used as synapses in a spiking neural network performing unsupervised learning. They learn using a simplified and customized “spike timing dependent plasticity” rule. In the network, neurons' threshold is adjusted following a homeostasis-type rule. We perform system level simulations with an experimentally verified model of the memristive devices' behavior. They show, on the textbook case of character recognition, that performance can compare with traditional supervised networks of similar complexity. They also show that the system can retain functionality with extreme variations of various memristive devices' parameters (a relative standard dispersion of more than 50% is tolerated on all device parameters), thanks to the robustness of the scheme, its unsupervised nature, and the capability of homeostasis. Additionally the network can adjust to stimuli presented with different coding schemes, is particularly robust to read disturb effects and does not require unrealistic control on the devices' conductance. These results open the way for a novel design approach for ultraadaptive electronic systems.
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Citations
Cites
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Strukov, D. B., Snider, G. S., Stewart, D. R., & Williams, R. S. (2008) · Nature · doi:10.1038/nature06932 · Cited by 11,828
Nanoscale Memristor Device as Synapse in Neuromorphic Systems
Jo, S. H., Chang, T., Ebong, I., Bhadviya, B. B., Mazumder, P., & Lu, W. (2010) · Nano Letters · doi:10.1021/nl904092h · Cited by 4,174 · cited 10×
“It has been suggested [9], [10], [11], [12], and shown experimentally [13], [14], [15], [16], [17], [18], that such devices could reproduce a learning rule of biological synapses – spike timing dependent plasticity (STDP) [19], [20] – that is believed to be a foundation of learning in the brain [21].”
BackgroundInfluentialA 128$\times$128 120 dB 15 $\mu$s Latency Asynchronous Temporal Contrast Vision Sensor
Lichtsteiner, P., Posch, C., & Delbruck, T. (2008) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2007.914337 · Cited by 2,485
‘Memristive’ switches enable ‘stateful’ logic operations via material implication
Borghetti, J., Snider, G. S., Kuekes, P. J., Yang, J. J., Stewart, D. R., & Williams, R. S. (2010) · Nature · doi:10.1038/nature08940 · Cited by 1,929
Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing
Kuzum, D., Jeyasingh, R. G. D., Lee, B., & Wong, H.-S. P. (2012) · Nano Letters · doi:10.1021/nl201040y · Cited by 1,267 · cited 3×
“It has been suggested [9], [10], [11], [12], and shown experimentally [13], [14], [15], [16], [17], [18], that such devices could reproduce a learning rule of biological synapses – spike timing dependent plasticity (STDP) [19], [20] – that is believed to be a foundation of learning in the brain [21].”
BackgroundKim, K.-H., Gaba, S., Wheeler, D., Cruz-Albrecht, J. M., Hussain, T., Srinivasa, N., & Lu, W. (2012) · Nano Letters · doi:10.1021/nl203687n · Cited by 924
Yu, S., Wu, Y., Jeyasingh, R., Kuzum, D., & Wong, H.-S. P. (2011) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2011.2147791 · Cited by 805
High precision tuning of state for memristive devices by adaptable variation-tolerant algorithm
Alibart, F., Gao, L., Hoskins, B. D., & Strukov, D. B. (2012) · Nanotechnology · doi:10.1088/0957-4484/23/7/075201 · Cited by 557 · cited 2×
“resistive RAMs and memristors [1], [2], or adaptive transistors [8], [3].”
BackgroundSpike-timing-dependent learning in memristive nanodevices
Snider, G. S. (2008) · 2008 IEEE International Symposium on Nanoscale Architectures · doi:10.1109/nanoarch.2008.4585796 · Cited by 341 · cited 17×
“This kind of robustness to spikes’ precise timing is consistent with computational neuroscience works on STDP [32].”
BackgroundMethodInfluential
Cited by
Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
Diehl, P. U., Neil, D., Binas, J., Cook, M., Liu, S.-C., & Pfeiffer, M. (2015) · 2015 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2015.7280696 · Cited by 991
Artificial optic-neural synapse for colored and color-mixed pattern recognition
Seo, S., Jo, S.-H., Kim, S., Shim, J., Oh, S., Kim, J.-H., Heo, K., Choi, J.-W., Choi, C., Oh, S., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07572-5 · Cited by 731
Hardware implementation of memristor-based artificial neural networks
Aguirre, F., Sebastian, A., Le Gallo, M., Song, W., Wang, T., Yang, J. J., Lu, W., Chang, M.-F., Ielmini, D., Yang, Y., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45670-9 · Cited by 564
“This neural network type is simpler than those considered in other more complex memristive ANNs, e.g. Multi-layer Perceptron (MLP) 54,196,267 , Convolutional Neural Networks (CNNs) 268 , Spiking Neural Networks (SNNs) 269 , among others (see Table 5).”
Spin-Transfer Torque Magnetic Memory as a Stochastic Memristive Synapse for Neuromorphic Systems
Vincent, A. F., Larroque, J., Locatelli, N., Ben Romdhane, N., Bichler, O., Gamrat, C., Zhao, W. S., Klein, J.-O., Galdin-Retailleau, S., & Querlioz, D. (2015) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2015.2414423 · Cited by 360
Self-citationMemristor-Based Multilayer Neural Networks With Online Gradient Descent Training
Soudry, D., Di Castro, D., Gal, A., Kolodny, A., & Kvatinsky, S. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2383395 · Cited by 271
Metadata
- Authors
- Querlioz, D., Bichler, O., Dollfus, P., & Gamrat, C.
- Venue
- IEEE Transactions on Nanotechnology
- Published
- 2013-05
- Volume
- 12
- Issue
- 3
- Pages
- 288-295
- ISSN
- 1536-125X (ISSN-L)
- 1941-0085
- OpenAlex
- W2162827630
- Cited by
- 420
- References
- 12
- FWCI
- 9.79
- Type
- journal article
- Language
- en
- Open access
- green
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