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Neuromorphic computing

Monolithic 3D stacking for neural network acceleration

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A 3D stackable computing-in-memory array that is based on resistive random-access memory could accelerate the implementation of machine learning algorithms.

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Fig. 1: 2D and 3D CIM architectures.

References

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA

    Bokyung Kim & Hai Li

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  1. Bokyung Kim
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  2. Hai Li
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Correspondence to Hai Li.

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The authors declare no competing interests.

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Kim, B., Li, H. Monolithic 3D stacking for neural network acceleration. Nat Electron 6, 937–938 (2023). https://doi.org/10.1038/s41928-023-01098-5

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