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A crossbar array of magnetoresistive memory devices for in-memory computing

Nature volume 601, pages 211–216 (2022)Cite this article

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Abstract

Implementations of artificial neural networks that borrow analogue techniques could potentially offer low-power alternatives to fully digital approaches1,2,3. One notable example is in-memory computing based on crossbar arrays of non-volatile memories4,5,6,7 that execute, in an analogue manner, multiply–accumulate operations prevalent in artificial neural networks. Various non-volatile memories—including resistive memory8,9,10,11,12,13, phase-change memory14,15 and flash memory16,17,18,19—have been used for such approaches. However, it remains challenging to develop a crossbar array of spin-transfer-torque magnetoresistive random-access memory (MRAM)20,21,22,  despite the technology’s practical advantages such as endurance and large-scale commercialization5. The difficulty stems from the low resistance of MRAM, which would result in large power consumption in a conventional crossbar array that uses current summation for analogue multiply–accumulate operations. Here we report a 64 × 64 crossbar array based on MRAM cells that overcomes the low-resistance issue with an architecture that uses resistance summation for analogue multiply–accumulate operations. The array is integrated with readout electronics in 28-nanometre complementary metal–oxide–semiconductor technology. Using this array, a two-layer perceptron is implemented to classify 10,000 Modified National Institute of Standards and Technology digits with an accuracy of 93.23 per cent (software baseline: 95.24 per cent). In an emulation of a deeper, eight-layer Visual Geometry Group-8 neural network with measured errors, the classification accuracy improves to 98.86 per cent (software baseline: 99.28 per cent). We also use the array to implement a single layer in a ten-layer neural network to realize face detection with an accuracy of 93.4 per cent.

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Fig. 1: MRAM crossbar array.
Fig. 2: Characterization of the MRAM crossbar array.
Fig. 3: Classification of 10,000 MNIST handwritten digits with a two-layer perceptron neural network.
Fig. 4: Face detection for always-on sensing applications in edge devices.

Data availability

The data that support the findings of this study are available from the corresponding authors upon reasonable request.

Code availability

Computer codes are available from the corresponding authors on reasonable request.

Change history

  • 14 January 2022

    In the version of this article initially published online, the posted article PDF was an earlier, incorrect version of the final article. The correct PDF has been posted as of 14 January 2022

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Acknowledgements

We thank G. Jin (Corporate President of Samsung Electronics), S. Hwang (CEO of Samsung SDS), E. Shim (Corporate EVP of Samsung Electronics) and G. Jeong (Corporate EVP of Samsung Electronics) for technical discussions and support.

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Authors and Affiliations

  1. Samsung Advanced Institute of Technology, Samsung Electronics, Suwon-si, South Korea

    Seungchul Jung, Hyungwoo Lee, Sungmeen Myung, Hyunsoo Kim, Seung Keun Yoon, Soon-Wan Kwon, Yongmin Ju, Minje Kim, Wooseok Yi, Changkyu Choi, Donhee Ham & Sang Joon Kim

  2. Foundry Business, Samsung Electronics, Yongin-si, South Korea

    Shinhee Han, Baeseong Kwon, Boyoung Seo & Kangho Lee

  3. Semiconductor R&D Center, Samsung Electronics, Hwaseong-si, South Korea

    Kilho Lee, Gwan-Hyeob Koh & Yoonjong Song

  4. John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

    Donhee Ham

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Contributions

S.J., G.-H.K., Y.S., D.H. and S.J.K. devised this work. S.J., H.L., S.M. and Y.J. designed the analogue circuits, and S.-W.K. and M.K. designed the digital circuits for the CMOS chips. S.H., B.K., B.S., Kilho Lee, Kangho Lee, G.-H.K. and Y.S. characterized the MTJs and optimized the fabrication steps for the MRAM crossbar array. S.J. and Y.J. developed evaluation boards. S.J., H.L., S.M. and W.Y. evaluated the MRAM crossbar array. H.K. and C.C. designed and trained the two-layer, eight-layer and ten-layer neural networks. S.J., S.K.Y. and W.Y. performed the MNIST classification experiments. S.J., S.K.Y., S.J.K. and D.H. analysed dot product errors. S.K.Y. and H.K. developed the emulator. S.-W.K. and M.K. developed the face detection system and performed the face detection experiments. S.J., H.L., S.K.Y., S.J.K. and D.H. wrote the paper. G.-H.K., Y.S., C.C., D.H. and S.J.K. supervised this work. All authors read and discussed the manuscript.

Corresponding authors

Correspondence to Donhee Ham or Sang Joon Kim.

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Nature thanks the anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Conventional memory crossbar array for ANN computing.

a, Conventional memory crossbar array to perform analogue vector–matrix multiplication. b, Vector–matrix multiplication is prevalent in ANN computing: it is used to transfer data from a layer to the next.

Extended Data Fig. 2 MTJ write/read operation.

a, For each column, two write/read data lines were added with access switches. b, Example of write operation. c, Example of read operation.

Extended Data Fig. 3 Estimated R from data-dependent delay and associated error.

Estimated R = τ/C calculated from equations (3) and (5) versus true R calculated from equation (4) for a broad variety of IN vectors, with W = RH for the entire column.

Extended Data Fig. 4 Error distribution after digital offsets.

Error distribution modified from Fig. 2d after applying digital offsets to select columns.

Extended Data Fig. 5 Performance with varying conditions.

a, Measured power efficiency and mean absolute error of the dot products as a function of the supply voltage of the TDC readout electronics for an 11.1 MHz operating frequency. b, Measured power efficiency and mean absolute error of the dot products as a function of the operating frequency for a 1.0 V supply voltage for the TDC readout electronics. For both a and b, each mean absolute error is obtained from 1,600 dot products as in Option 1 in Extended Data Table 1, except for the mean absolute error in the case of the 1.0 V TDC readout supply and the 11.1 MHz operating frequency, for which the error is calculated from ~4 million dot products (this Option 2 in Extended Data Table 1).

Extended Data Fig. 6 Measurement set-up.

a, Evaluation board containing voltage regulators, clock generators, an MCU and the MRAM crossbar array chip. b, The MCU communicates with the PC via USB.

Extended Data Fig. 7 Distribution of dot product errors.

a, NC = 1 and NΔ = −16 data group. b, NC = 1 and NΔ = 16 data group.

Extended Data Table 1 Measured performance summary
Full size table

Supplementary information

Supplementary Video 1

This video demonstrates a real-time face detection using the system of Fig. 4c, including a webcam. The stream of images captured by the webcam are shown on the bottom left. When a face appears, the neural network detects it and a smile icon shows on the top left. As the face disappears, the neural network recog-nizes it and the smile icon disappears. The graph on the bottom right shows the dissipated power in the four MRAM crossbar arrays combined. The 10-layer VGG-like neural network model used here is shown on the top right, where the 7th convolutional layer is realized by using 4 MRAM crossbar arrays.

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Jung, S., Lee, H., Myung, S. et al. A crossbar array of magnetoresistive memory devices for in-memory computing. Nature 601, 211–216 (2022). https://doi.org/10.1038/s41586-021-04196-6

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