Memristors Development and In-Memory Computing Architecture
DOI:
https://doi.org/10.54097/mmxsdt25Keywords:
Memristors, computing in memory, development.Abstract
With the development of neural network, Von Neumann's bottleneck is hanging over heads. The emergence of memristor and computing in memory architecture has greatly improved data processing speed and energy efficiency. This review provides a brief literature review of memristors, focus on development, and provide applications, challenges, and prospects of memristors. Firstly, the process of memristor research is introduced. Secondly, this paper highlights memristors development, which is divided into three time periods: 2008 before, 2008 to 2013, and 2013 to 2018. In 1971, Cai Shaotang predicted the existence of memristors. The Stan William team of HP Laboratory first prepared a memristor through experiments in 2008. In 2015, UCSB used a 12 times 12 array to realize the recognition of three letters. Memristor optimization work of the Tsinghua University team is introduced. Thirdly, this paper discusses the applications, and challenges of memristors and gives useful advice on optimization work in the future. Application is divided into 3 parts. In addition to many other difficulties in circuit and system designs, memristive neural networks require much larger array sizes and peripheral circuit integration with computing kernels for maximum energy and throughput efficiency in order to be used for more complex computing tasks.
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References
Chua, L. "Memristor-the missing circuit element." IEEE Transactions on circuit theory 18.5: 507-519 (1971).
Williams, R., "How we found the missing memristor," IEEE Spectr. (45) 28–35 (2008).
Strukov, D. B., Snider, G. S., Stewart, D. R., & Williams, R. S. "The missing memristor found." nature 453.7191: 80-83 (2008).
Gale, E. "TiO2-based memristors and ReRAM: materials, mechanisms and models (a review)." Semiconductor Science and Technology 29.10: 104004 (2014).
Yao, P., Wu, H., Gao, B., Eryilmaz, S. B., Huang, X., Zhang, W., ... & Qian, H. "Face classification using electronic synapses." Nature communications 8.1: 15199(2017).
Xia, Q., & Yang, J. J. "Memristive crossbar arrays for brain-inspired computing." Nature materials 18.4: 309-323 (2019).
Mehonic, A., Sebastian, A., Rajendran, B., Simeone, O., Vasilaki, E., & Kenyon, A. J. "Memristors—From in‐memory computing, deep learning acceleration, and spiking neural networks to the future of neuromorphic and bio‐inspired computing." Advanced Intelligent Systems 2.11: 2000085(2020).
Kumar, S., Wang, X., Strachan, J. P., Yang, Y., & Lu, W. D. "Dynamical memristors for higher-complexity neuromorphic computing." Nature Reviews Materials 7.7: 575-591 (2022).
Li Y, Ang K W. "Hardware implementation of neuromorphic computing using large‐scale memristor crossbar arrays." Advanced Intelligent Systems 3.1: 2000137(2021).
Zhang, W., Yao, P., Gao, B., Liu, Q., Wu, D., Zhang, Q., ... & Wu, H. "Edge learning using a fully integrated neuro-inspired memristor chip." Science 381.6663: 1205-1211(2023).
Nawrocki, Robert A., Richard M. Voyles, and Sean E. Shaheen. "A mini review of neuromorphic architectures and implementations." IEEE Transactions on Electron Devices 63.10: 3819-3829(2016).
O. Mutlu, S. Ghose, J. Gómez-Luna, R. Ausavarungnirun, Microprocess. Microsyst. 67, 2 8 (2019)
Ielmini, D., & Wong, H. S. P. "In-memory computing with resistive switching devices." Nature electronics 1.6: 333-343 (2018).
Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R., & Eleftheriou, E. "Memory devices and applications for in-memory computing." Nature nanotechnology 15.7: 529-544(2020).
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., ... & Hassabis, D. "Mastering the game of Go with deep neural networks and tree search." nature 529.7587: 484-489(2016).
Wang, W., Pedretti, G., Milo, V., Carboni, R., Calderoni, A., Ramaswamy, N., ... & Ielmini, D. Learning of spatiotemporal patterns in a spiking neural network with resistive switching synapses. Sci. Adv. 4, eaat4752 (2018).
Jo, S. H., Chang, T., Ebong, I., Bhadviya, B. B., Mazumder, P., & Lu, W. Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett. 10, 1297–1301 (2010).
Ohno, T., Hasegawa, T., Tsuruoka, T., Terabe, K., Gimzewski, J. K., & Aono, M. Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. Nat. Mater. 10, 591–595 (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. Elect. Dev. 58, 2729–2737 (2011).
Wang, Z. Q., Xu, H. Y., Li, X. H., Yu, H., Liu, Y. C., & Zhu, X. J. Synaptic learning and memory functions achieved using oxygen ion migration/diffusion in an amorphous InGaZnO memristor. Adv. Funct. Mater. 22, 2759–2765 (2012).
La Barbera, S., Vuillaume, D. & Alibart, F. Filamentary switching: synaptic plasticity through device volatility. ACS Nano 9, 941–949 (2015).
Pickett, M. D., Medeiros-Ribeiro, G. & Williams, R. S. A scalable neuristor built with Mott memristors. Nat. Mater. 12, 114–117 (2013).
Zidan, Mohammed A., John Paul Strachan, and Wei D. Lu. "The future of electronics based on memristive systems." Nature electronics 1.1: 22-29(2018).
Li, C. et al. "Analogue signal and image processing with large memristor crossbars." Nat. Electron. 1, 52–59 (2018).
Li, C. et al. "Efcient and self-adaptive in-situ learning in multilayer memristor neural networks." Nat. Commun. 9, 2385 (2018).
Li, C. et al. "Long short-term memory networks in memristor crossbar arrays." Nat. Mach. Intell. 1, 49–57 (2019).
Hu, M. et al. "Memristor-based analog computation and neural network classifcation with a dot product engine." Adv. Mater. 30, 1705914 (2018).
Jiang, H. et al. "Sub-10 nm Ta channel responsible for superior performance of a HfO2 memristor." Sci. Rep. 6, 28525 (2016).
Prezioso, M. et al. "Training and operation of an integrated neuromorphic network based on metal–oxide memristors." Nature 521, 61–64 (2015).
Bayat, F. M. "Implementation of multilayer perceptron network with highly uniform passive memristive crossbar circuits." Nat. Commun. 9, 2331 (2018).
Sheridan, P. M. et al. "Sparse coding with memristor networks." Nat. Nanotechnol. 12, 784–789 (2017).
Du, C. et al. "Reservoir computing using dynamic memristors for temporal information processing." Nat. Commun. 8, 2204 (2017).
Serb, A. et al. "Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses." Nat. Commun. 7, 12611 (2016).
Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R., & Eleftheriou, E. "Memory devices and applications for in-memory computing." Nature nanotechnology 15.7: 529-544(2020).
Lanza, M., Wong, H. S. P., Pop, E., Ielmini, D., Strukov, D., Regan, B. C., ... & Shi, Y. "Recommended Methods to Study Resistive Switching Devices http://researchonline. ljmu. ac. uk/id/eprint/9642."
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