The DOI matches its Crossref record; 122 of the 122 references it cites were found in a registry or an index.
Memristor‐Based Neuromorphic Chips
Abstract
In the era of information, characterized by an exponential growth in data volume and an escalating level of data abstraction, there has been a substantial focus on brain‐like chips, which are known for their robust processing power and energy‐efficient operation. Memristors are widely acknowledged as the optimal electronic devices for the realization of neuromorphic computing, due to their innate ability to emulate the interconnection and information transfer processes witnessed among neurons. This review paper focuses on memristor‐based neuromorphic chips, which provide an extensive description of the working principle and characteristic features of memristors, along with their applications in the realm of neuromorphic chips. Subsequently, a thorough discussion of the memristor array, which serves as the pivotal component of the neuromorphic chip, as well as an examination of the present mainstream neural networks, is delved. Furthermore, the design of the neuromorphic chip is categorized into three crucial sections, including synapse‐neuron cores, networks on chip (NoC), and neural network design. Finally, the key performance metrics of the chip is highlighted, as well as the key metrics related to the memristor devices are employed to realize both the synaptic and neuronal components.
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Cites
Hodgkin, A. L., & Huxley, A. F. (1952) · The Journal of Physiology · doi:10.1113/jphysiol.1952.sp004764 · Cited by 23,191
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
Memristor—The Missing Circuit Element
Chua, L. (1971) · IEEE Transactions on Circuit Theory · doi:10.1109/tct.1971.1083337 · Cited by 10,268
Nanoionics-based resistive switching memories
Waser, R., & Aono, M. (2007) · Nature Materials · doi:10.1038/nmat2023 · Cited by 4,923
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
Loihi: A Neuromorphic Manycore Processor with On-Chip Learning
Davies, M., Srinivasa, N., Lin, T.-H., Chinya, G., Cao, Y., Choday, S. H., Dimou, G., Joshi, P., Imam, N., Jain, S., et al. (2018) · IEEE Micro · doi:10.1109/mm.2018.112130359 · Cited by 3,897
Networks of spiking neurons: The third generation of neural network models
Maass, W. (1997) · Neural Networks · doi:10.1016/s0893-6080(97)00011-7 · Cited by 3,481
Wong, H.-S. P., Lee, H.-Y., Yu, S., Chen, Y.-S., Wu, Y., Chen, P.-S., Lee, B., Chen, F. T., & Tsai, M.-J. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2190369 · Cited by 2,757
Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing
Wang, Z., Joshi, S., Savel’ev, S. E., Jiang, H., Midya, R., Lin, P., Hu, M., Ge, N., Strachan, J. P., Li, Z., et al. (2017) · Nature Materials · doi:10.1038/nmat4756 · Cited by 2,358
Cited by
Kim, M., Kim, D.-E., Wang, Y., Lee, D., Lim, D.-H., Choi, H., Kymissis, I., Yang, J. J., Suh, J., Lee, H.-S., et al. (2024) · Applied Materials Today · doi:10.1016/j.apmt.2024.102204 · Cited by 24
Metadata
- Authors
- Duan, X., Cao, Z., Gao, K., Yan, W., Sun, S., Zhou, G., Wu, Z., Ren, F., & Sun, B.
- Venue
- Advanced Materials
- Publisher
- Wiley
- Published
- 2024-01-02
- Volume
- 36
- Issue
- 14
- Pages
- e2310704-e2310704
- ISSN
- 0935-9648 (ISSN-L)
- 1521-4095
- OpenAlex
- W4390540350
- Cited by
- 432
- References
- 122
- FWCI
- 44.56
- Type
- journal article
- Language
- en
- Open access
- closed
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- Duan, X., Cao, Z., Gao, K., Yan, W., Sun, S., Zhou, G., Wu, Z., Ren, F., & Sun, B. (2024). Memristor‐Based Neuromorphic Chips. Advanced Materials. https://doi.org/10.1002/adma.202310704
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