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Transforming edge hardware with in situ learning features

Nature Reviews Electrical Engineering volume 1, pages 141–142 (2024)Cite this article

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Memristor devices have shown notable superiority in the realm of neuromorphic computing chips, particularly in artificial intelligence (AI) inference tasks. Researchers are now grappling with the intricacies of incorporating in situ learning capabilities into memristor-based chips, paving the way for more powerful edge intelligence.

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References

  1. Yao, P. et al. Face classification using electronic synapses. Nat. Commun. 8, 15199 (2017).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  2. Zhang, W. et al. Neuro-inspired computing chips. Nat. Electron. 3, 371–382 (2020).

    Article  ADS  Google Scholar 

  3. Wan, W. et al. A compute-in-memory chip based on resistive random-access memory. Nature 608, 504–512 (2022).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  4. Yao, P. et al. Fully hardware-implemented memristor convolutional neural network. Nature 577, 641–646 (2020).

    Article  CAS  PubMed  ADS  Google Scholar 

  5. Ambrogio, S. et al. An analog-AI chip for energy-efficient speech recognition and transcription. Nature 620, 768–775 (2023).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  6. Jung, S. et al. A crossbar array of magnetoresistive memory devices for in-memory computing. Nature 601, 211–216 (2022).

    Article  CAS  PubMed  ADS  Google Scholar 

  7. Rao, M. et al. Thousands of conductance levels in memristors integrated on CMOS. Nature 615, 823–829 (2023).

    Article  CAS  PubMed  ADS  Google Scholar 

  8. Zhang, W. et al. Edge learning using a fully integrated neuro-inspired memristor chip. Science 381, 1205–1211 (2023).

    Article  CAS  PubMed  ADS  Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge support from the National Natural Science Foundation of China (62025111 and 92064001).

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Authors and Affiliations

  1. School of Integrated Circuits, Tsinghua University, Beijing, China

    Peng Yao, Bin Gao & Huaqiang Wu

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  1. Peng Yao
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  2. Bin Gao
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  3. Huaqiang Wu
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Correspondence to Bin Gao.

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The authors declare no competing interests.

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Yao, P., Gao, B. & Wu, H. Transforming edge hardware with in situ learning features. Nat Rev Electr Eng 1, 141–142 (2024). https://doi.org/10.1038/s44287-024-00031-y

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