The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Edge learning using a fully integrated neuro-inspired memristor chip
Abstract
Learning is highly important for edge intelligence devices to adapt to different application scenes and owners. Current technologies for training neural networks require moving massive amounts of data between computing and memory units, which hinders the implementation of learning on edge devices. We developed a fully integrated memristor chip with the improvement learning ability and low energy cost. The schemes in the STELLAR architecture, including its learning algorithm, hardware realization, and parallel conductance tuning scheme, are general approaches that facilitate on-chip learning by using a memristor crossbar array, regardless of the type of memristor device. Tasks executed in this study included motion control, image classification, and speech recognition.
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Training and operation of an integrated neuromorphic network based on metal-oxide memristors
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Cited by
Memristor‐Based Neuromorphic Chips
Duan, X., Cao, Z., Gao, K., Yan, W., Sun, S., Zhou, G., Wu, Z., Ren, F., & Sun, B. (2024) · Advanced Materials · doi:10.1002/adma.202310704 · Cited by 432
Large-scale photonic chiplet Taichi empowers 160-TOPS/W artificial general intelligence
Xu, Z., Zhou, T., Ma, M., Deng, C., Dai, Q., & Fang, L. (2024) · Science · doi:10.1126/science.adl1203 · Cited by 308
Metadata
- Authors
- Zhang, W., Yao, P., Gao, B., Liu, Q., Wu, D., Zhang, Q., Li, Y., Qin, Q., Li, J., Zhu, Z., Cai, Y., Wu, D., Tang, J., Qian, H., Wang, Y., & Wu, H.
- Venue
- Science
- Published
- 2023-09-15
- Volume
- 381
- Issue
- 6663
- Pages
- 1205-1211
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W4386736788
- Cited by
- 437
- References
- 46
- FWCI
- 32.03
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zhang, W., Yao, P., Gao, B., Liu, Q., Wu, D., Zhang, Q., Li, Y., Qin, Q., Li, J., Zhu, Z., Cai, Y., Wu, D., Tang, J., Qian, H., Wang, Y., & Wu, H. (2023). Edge learning using a fully integrated neuro-inspired memristor chip. Science. https://doi.org/10.1126/science.ade3483
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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