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Reinforcement learning with analogue memristor arrays
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Cites
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Self-citationHuman-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
Strukov, D. B., Snider, G. S., Stewart, D. R., & Williams, R. S. (2008) · Nature · doi:10.1038/nature06932 · Cited by 11,828
Self-citationContinuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1509.02971 · Cited by 10,782
Mastering the game of Go without human knowledge
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al. (2017) · Nature · doi:10.1038/nature24270 · Cited by 9,084
In-Datacenter Performance Analysis of a Tensor Processing Unit
Jouppi, N. P., Young, C., Patil, N., Patterson, D., Agrawal, G., Bajwa, R., Bates, S., Bhatia, S., Boden, N., Borchers, A., et al. (2017) · Proceedings of the 44th Annual International Symposium on Computer Architecture · doi:10.1145/3079856.3080246 · Cited by 4,537
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
Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks
Chen, Y.-H., Krishna, T., Emer, J. S., & Sze, V. (2017) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2016.2616357 · Cited by 3,247
Training and operation of an integrated neuromorphic network based on metal-oxide memristors
Prezioso, M., Merrikh-Bayat, F., Hoskins, B. D., Adam, G. C., Likharev, K. K., & Strukov, D. B. (2015) · Nature · doi:10.1038/nature14441 · Cited by 3,001
Research works printed but not linked: 26
- Sutton, R. S. & Barto, A. G. Reinforcement Learning: An Introduction. Second Edition (MIT Press, Cambridge, 2018).
- Chen, Y. et al. Dadiannao: A machine-learning supercomputer. In Proceedings of the 47th Annual IEEE/ACM International Symposium on Microarchitecture 609–622 (IEEE Computer Society, 2014). · DOI · Paper page
- 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). · DOI · Paper page
- Eryilmaz, S. B. et al. Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array. Front. Neurosci. 8, 205 (2014). · DOI · Paper page
- Ambrogio, S. et al. Unsupervised learning by spike timing dependent plasticity in phase change memory (PCM) synapses. Front. Neurosci. 10, 56 (2016). · DOI · Paper page
- Pershin, Y. V. & Di Ventra, M. Neuromorphic, digital, and quantum computation with memory circuit elements. Proc. IEEE 100, 2071–2080 (2012). · DOI · Paper page
- Lim, H., Kim, I., Kim, J. S., Hwang, C. S. & Jeong, D. S. Short-term memory of TiO2-based electrochemical capacitors: empirical analysis with adoption of a sliding threshold. Nanotechnology 24, 384005 (2013). · DOI
- Sheridan, P., Ma, W. & Lu, W. Pattern recognition with memristor networks. In 2014 IEEE International Symposium on Circuits and Systems (ISCAS) 1078–1081 (IEEE, 2014). · DOI
- La Barbera, S., Vuillaume, D. & Alibart, F. Filamentary switching: synaptic plasticity through device volatility. ACS Nano 9, 941–949 (2015). · DOI · Paper page
- Hu, S. G. et al. Associative memory realized by a reconfigurable memristive Hopfield neural network. Nat. Commun. 6, 7522 (2015). · DOI · Paper page
- Serb, A. et al. Unsupervised learning in probabilistic neural networks with multi-state metal–oxide memristive synapses. Nat. Commun. 7, 12611 (2016). · DOI · Paper page
- Park, J. et al. TiOx-based RRAM synapse with 64-levels of conductance and symmetric conductance change by adopting a hybrid pulse scheme for neuromorphic computing. IEEE Elect. Dev. Lett. 37, 1559–1562 (2016). · DOI · Paper page
- Hu, M. et al. Dot-product engine for neuromorphic computing: programming 1T1M crossbar to accelerate matrix-vector multiplication. In 53rd ACM/EDAC/IEEE Design Automation Conference (DAC) 1–6 (IEEE, 2016). · DOI · Paper page
- Sheridan, P. M. et al. Sparse coding with memristor networks. Nat. Nanotechnol. 12, 784–789 (2017). · DOI · Paper page
- Zidan, M. A. et al. A general memristor-based partial differential equation solver. Nat. Electron. 1, 411–420 (2018). · DOI · Paper page
- Nili, H. et al. Hardware-intrinsic security primitives enabled by analogue state and nonlinear conductance variations in integrated memristors. Nat. Electron. 1, 197–202 (2018). · DOI · Paper page
- Chen, W.-H. et al. A 65nm 1Mb nonvolatile computing-in-memory ReRAM macro with sub-16ns multiply-and-accumulate for binary DNN AI edge processors. In 2018 IEEE International Solid-State Circuits Conference (ISSCC) 494–496 (IEEE, 2018). · DOI · Paper page
- Jeong, Y., Lee, J., Moon, J., Shin, J. H. & Lu, W. D. K-means data clustering with memristor networks. Nano Lett. 18, 4447–4453 (2018). · DOI
- Nandakumar, S. et al. Mixed-precision architecture based on computational memory for training deep neural networks. In 2018 IEEE International Symposium on Circuits and Systems (ISCAS) 1–5 (IEEE, 2018). · DOI
- Choi, S., Shin, J. H., Lee, J., Sheridan, P. & Lu, W. D. Experimental demonstration of feature extraction and dimensionality reduction using memristor networks. Nano Lett. 17, 3113–3118 (2017). · DOI · Paper page
- Barto, A. G., Sutton, R. S. & Anderson, C. W. Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Trans. Syst. Man Cybern. SMC-13, 834–846, (1983). · DOI · Paper page
- Sutton, R. S. Generalization in reinforcement learning: successful examples using sparse coarse coding. In Advances in Neural Information Processing Systems 8 1038–1044 (NIPS, 1996).
- Jiang, H. et al. Sub-10 nm Ta channel responsible for superior performance of a HfO2 memristor. Sci. Rep. 6, 28525 (2016). · DOI · Paper page
- Tieleman, T. & Hinton, G. Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning 4, 26–31 (2012).
- Choi, S. et al. SiGe epitaxial memory for neuromorphic computing with reproducible high performance based on engineered dislocations. Nat. Mater. 17, 335–340 (2018). · DOI · Paper page
- LeCun, Y., Touresky, D., Hinton, G. & Sejnowski, T. A theoretical framework for back-propagation. In Proceedings of the 1988 Connectionist Models Summer School 21–28 (CMU, Pittsburgh, PA, Morgan Kaufmann, 1988).
Cited by
Resistive switching materials for information processing
Wang, Z., Wu, H., Burr, G. W., Hwang, C. S., Wang, K. L., Xia, Q., & Yang, J. J. (2020) · Nature Reviews Materials · doi:10.1038/s41578-019-0159-3 · Cited by 1,214
Self-citationNeuro-inspired computing chips
Zhang, W., Gao, B., Tang, J., Yao, P., Yu, S., Chang, M.-F., Yoo, H.-J., Qian, H., & Wu, H. (2020) · Nature Electronics · doi:10.1038/s41928-020-0435-7 · Cited by 892
Tang, J., Yuan, F., Shen, X., Wang, Z., Rao, M., He, Y., Sun, Y., Li, X., Zhang, W., Li, Y., et al. (2019) · Advanced Materials · doi:10.1002/adma.201902761 · Cited by 834
Self-citationA comprehensive review on emerging artificial neuromorphic devices
Zhu, J., Zhang, T., Yang, Y., & Huang, R. (2020) · Applied Physics Reviews · doi:10.1063/1.5118217 · Cited by 758
Edge learning using a fully integrated neuro-inspired memristor chip
Zhang, W., Yao, P., Gao, B., Liu, Q., Wu, D., Zhang, Q., Li, Y., Qin, Q., Li, J., Zhu, Z., et al. (2023) · Science · doi:10.1126/science.ade3483 · Cited by 437
Self-citation
Metadata
- Authors
- Wang, Z., Li, C., Song, W., Rao, M., Belkin, D., Li, Y., Yan, P., Jiang, H., Lin, P., Hu, M., Strachan, J. P., Ge, N., Barnell, M., Wu, Q., Barto, A. G., Qiu, Q., Williams, R. S., Xia, Q., & Yang, J. J.
- Venue
- Nature Electronics
- Published
- 2019-03-15
- Volume
- 2
- Issue
- 3
- Pages
- 115-124
- ISSN
- 2520-1131 (ISSN-L)
- OpenAlex
- W2922168646
- Cited by
- 370
- References
- 25
- FWCI
- 17.89
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
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- Wang, Z., Li, C., Song, W., Rao, M., Belkin, D., Li, Y., Yan, P., Jiang, H., Lin, P., Hu, M., Strachan, J. P., Ge, N., Barnell, M., Wu, Q., Barto, A. G., Qiu, Q., Williams, R. S., Xia, Q., & Yang, J. J. (2019). Reinforcement learning with analogue memristor arrays. Nature Electronics. https://doi.org/10.1038/s41928-019-0221-6
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