Neuro-inspired computing chips
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There is a lack of an efficient commercial tool for software-hardware co-design, because device-level simulators do not consider architecture-level and communication on the chip, while architecture-level simulators lack consideration of realistic device properties.
“There is a lack of an efficient commercial tool for software-hardware co-design, as device-level simulators do not consider architecture-level and communication on the chip, while architecture-level simulators lack the consideration of realistic device properties159.”
From Hardware implementation of memristor-based artificial neural networks (2024) · cites this paper as [159]
Energy efficiency is one of the main benchmarking metrics, besides computing density and computing accuracy.
“Energy efficiency is one of the main benchmarking metrics, besides computing density (performance per area) and computing accuracy, which were proposed in Ref. [234].”
From Brain-inspired computing systems: a systematic literature review (2024) · cites this paper as [234]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
Strukov, D. B., Snider, G. S., Stewart, D. R., & Williams, R. S. (2008) · Nature · doi:10.1038/nature06932 · Cited by 11,744 · see citing works
Quantum supremacy using a programmable superconducting processor
Arute, F., Arya, K., Babbush, R., Bacon, D., Bardin, J. C., Barends, R., Biswas, R., Boixo, S., Brandao, F. G. S. L., Buell, D. A., et al. (2019) · Nature · doi:10.1038/s41586-019-1666-5 · Cited by 7,720 · see citing works
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 · see citing works
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 · see citing works
Neuromorphic electronic systems
Mead, C. (1990) · Proceedings of the IEEE · doi:10.1109/5.58356 · Cited by 2,368 · see citing works
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 · see citing works
Fully hardware-implemented memristor convolutional neural network
Yao, P., Wu, H., Gao, B., Tang, J., Zhang, Q., Zhang, W., Yang, J. J., & Qian, H. (2020) · Nature · doi:10.1038/s41586-020-1942-4 · Cited by 2,179 · see citing works
Self-citationAnalogue signal and image processing with large memristor crossbars
Li, C., Hu, M., Li, Y., Jiang, H., Ge, N., Montgomery, E., Zhang, J., Song, W., Dávila, N., Graves, C. E., et al. (2017) · Nature Electronics · doi:10.1038/s41928-017-0002-z · Cited by 1,292 · see citing works
Equivalent-accuracy accelerated neural-network training using analogue memory
Ambrogio, S., Narayanan, P., Tsai, H., Shelby, R. M., Boybat, I., di Nolfo, C., Sidler, S., Giordano, M., Bodini, M., Farinha, N. C. P., et al. (2018) · Nature · doi:10.1038/s41586-018-0180-5 · Cited by 1,151 · see citing works
Efficient and self-adaptive in-situ learning in multilayer memristor neural networks
Li, C., Belkin, D., Li, Y., Yan, P., Hu, M., Ge, N., Jiang, H., Montgomery, E., Lin, P., Wang, Z., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04484-2 · Cited by 931 · see citing works
Neuromorphic computing with multi-memristive synapses
Boybat, I., Le Gallo, M., Nandakumar, S. R., Moraitis, T., Parnell, T., Tuma, T., Rajendran, B., Leblebici, Y., Sebastian, A., & Eleftheriou, E. (2018) · Nature Communications · doi:10.1038/s41467-018-04933-y · Cited by 903 · cited 1× · see citing works
“To overcome the limited precision of NVM devices, a general approach is using multiple devices with proportional significance to represent a multi-bit number 60,61 .”
MethodMemristor‐Based Analog Computation and Neural Network Classification with a Dot Product Engine
Hu, M., Graves, C. E., Li, C., Li, Y., Ge, N., Montgomery, E., Davila, N., Jiang, H., Williams, R. S., Yang, J. J., et al. (2018) · Advanced Materials · doi:10.1002/adma.201705914 · Cited by 751 · see citing works
Scaling for edge inference of deep neural networks
Xu, X., Ding, Y., Hu, S. X., Niemier, M., Cong, J., Hu, Y., & Shi, Y. (2018) · Nature Electronics · doi:10.1038/s41928-018-0059-3 · Cited by 462 · see citing works
Three-dimensional memristor circuits as complex neural networks
Lin, P., Li, C., Wang, Z., Li, Y., Jiang, H., Song, W., Rao, M., Zhuo, Y., Upadhyay, N. K., Barnell, M., et al. (2020) · Nature Electronics · doi:10.1038/s41928-020-0397-9 · Cited by 375 · see citing works
Biswas, A., & Chandrakasan, A. P. (2018) · 2018 IEEE International Solid - State Circuits Conference - (ISSCC) · doi:10.1109/isscc.2018.8310397 · Cited by 337 · see citing works
Liu, Q., Gao, B., Yao, P., Wu, D., Chen, J., Pang, Y., Zhang, W., Liao, Y., Xue, C.-X., Chen, W.-H., et al. (2020) · 2020 IEEE International Solid- State Circuits Conference - (ISSCC) · doi:10.1109/isscc19947.2020.9062953 · Cited by 308 · cited 1× · see citing works
“Although applying heuristic compensation by adding scaling factors to the output terminal can mitigate this effect for some specific neural networks 66 , The past milestones 11,12,17,21,23,63,71,105 and future prospects are illustrated.”
MethodSelf-citationXNOR-RRAM: A scalable and parallel resistive synaptic architecture for binary neural networks
Sun, X., Yin, S., Peng, X., Liu, R., Seo, J.-S., & Yu, S. (2018) · 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE) · doi:10.23919/date.2018.8342235 · Cited by 246 · see citing works
Self-citationPrezioso, M., Mahmoodi, M. R., Bayat, F. M., Nili, H., Kim, H., Vincent, A., & Strukov, D. B. (2018) · Nature Communications · doi:10.1038/s41467-018-07757-y · Cited by 215 · see citing works
Investigation of statistical retention of filamentary analog RRAM for neuromophic computing
Zhao, M., Wu, H., Gao, B., Zhang, Q., Wu, W., Wang, S., Xi, Y., Wu, D., Deng, N., Yu, S., et al. (2017) · 2017 IEEE International Electron Devices Meeting (IEDM) · doi:10.1109/iedm.2017.8268522 · Cited by 95 · see citing works
Self-citationWu, H., Yao, P., Gao, B., & Qian, H. (2018) · Nature Electronics · doi:10.1038/s41928-017-0011-y · Cited by 27 · see citing works
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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 499 · see citing works
Self-citationNing, H., Yu, Z., Zhang, Q., Wen, H., Gao, B., Mao, Y., Li, Y., Zhou, Y., Zhou, Y., Chen, J., et al. (2023) · Nature Nanotechnology · doi:10.1038/s41565-023-01343-0 · Cited by 198 · see citing works
Self-citationMemristive Crossbar Arrays for Storage and Computing Applications
Li, H., Wang, S., Zhang, X., Wang, W., Yang, R., Sun, Z., Feng, W., Lin, P., Wang, Z., Sun, L., et al. (2021) · Advanced Intelligent Systems · doi:10.1002/aisy.202100017 · Cited by 205 · see citing works
A fully hardware-based memristive multilayer neural network
Kiani, F., Yin, J., Wang, Z., Yang, J. J., & Xia, Q. (2021) · Science Advances · doi:10.1126/sciadv.abj4801 · Cited by 97 · see citing works
Echo state graph neural networks with analogue random resistive memory arrays
Wang, S., Li, Y., Wang, D., Zhang, W., Chen, X., Dong, D., Wang, S., Zhang, X., Lin, P., Gallicchio, C., et al. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00609-5 · Cited by 84 · see citing works
Self-citationArtificial Neural Networks Based on Memristive Devices: From Device to System
Huang, H.-M., Wang, Z., Wang, T., Xiao, Y., & Guo, X. (2020) · Advanced Intelligent Systems · doi:10.1002/aisy.202000149 · Cited by 82 · see citing works
A memristor-based Bayesian machine
Harabi, K.-E., Hirtzlin, T., Turck, C., Vianello, E., Laurent, R., Droulez, J., Bessière, P., Portal, J.-M., Bocquet, M., & Querlioz, D. (2022) · Nature Electronics · doi:10.1038/s41928-022-00886-9 · Cited by 58 · see citing works
A collective AI via lifelong learning and sharing at the edge
Soltoggio, A., Ben-Iwhiwhu, E., Braverman, V., Eaton, E., Epstein, B., Ge, Y., Halperin, L., How, J., Itti, L., Jacobs, M. A., et al. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00800-2 · Cited by 51 · see citing works
Brain-inspired computing systems: a systematic literature review
Zolfagharinejad, M., Alegre-Ibarra, U., Chen, T., Kinge, S., & van der Wiel, W. G. (2024) · The European Physical Journal B · doi:10.1140/epjb/s10051-024-00703-6 · Cited by 39 · see citing works
Controlled Majority-Inverter Graph Logic With Highly Nonlinear, Self-Rectifying Memristor
Ni, R., Yang, L., Huang, X.-D., Ren, S.-G., Wan, T.-Q., Li, Y., & Miao, X.-S. (2021) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2021.3106234 · Cited by 31 · see citing works
Thousands of conductance levels in memristors monolithically integrated on CMOS
Yang, J., Rao, M., Tang, H., Wu, J.-B., Song, W., Zhang, M., Yin, W., Zhuo, Y., Kiani, F., Chen, B., et al. (2022) · doi:10.21203/rs.3.rs-1939455/v1 · Cited by 504 · see citing works
- Authors
- Zhang, W., Gao, B., Tang, J., Yao, P., Yu, S., Chang, M.-F., Yoo, H.-J., Qian, H., & Wu, H.
- Affiliations
- Tsinghua University (CN)
- Beijing National Research Center for Information Science and Technology (CN)
- Georgia Institute of Technology (US)
- National Tsing Hua University (TW)
- Korea Advanced Institute of Science and Technology (KR)
- Corresponding author
- Wu, Huaqiang
- ORCID
- Venue
- Nature Electronics
- Published
- 2020-07-21
- Volume
- 3
- Issue
- 7
- Pages
- 371-382
- ISSN
- 2520-1131 (ISSN-L)
- OpenAlex
- W3044192339
- Cited by
- 892
- References
- 23
- Citations by year
- 2026: 96 · 2025: 219 · 2024: 204 · 2023: 163 · 2022: 116 · 2021: 86 · 2020: 8
- FWCI
- 32.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Electronics (publishedVersion)
- Funders
- Huawei Technologies (YBN2019075015)
- Beijing Municipal Science and Technology Commission (Z191100007519008)
- Ministry of Science and Technology of the People’s Republic of China (2019YFB2205104)
- National Natural Science Foundation of China (61851404)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Low-power high-performance VLSI design
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- neural network hardware, spiking neural networks, energy efficiency, non-volatile memory, on-chip learning, co-design, electronic design automation, benchmarking metrics
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