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

Implementations of artificial neural networks that borrow analogue techniques could potentially offer low-power alternatives to fully digital approaches 1 – 3 . One notable example is in-memory computing based on crossbar arrays of non-volatile memories 4 – 7 that execute, in an analogue manner, mul...

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Bibliographic Details
Published in:Nature (London) 2022-01, Vol.601 (7892), p.211-216
Main Authors: Jung, Seungchul, Lee, Hyungwoo, Myung, Sungmeen, Kim, Hyunsoo, Yoon, Seung Keun, Kwon, Soon-Wan, Ju, Yongmin, Kim, Minje, Yi, Wooseok, Han, Shinhee, Kwon, Baeseong, Seo, Boyoung, Lee, Kilho, Koh, Gwan-Hyeob, Lee, Kangho, Song, Yoonjong, Choi, Changkyu, Ham, Donhee, Kim, Sang Joon
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Language:English
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Summary:Implementations of artificial neural networks that borrow analogue techniques could potentially offer low-power alternatives to fully digital approaches 1 – 3 . One notable example is in-memory computing based on crossbar arrays of non-volatile memories 4 – 7 that execute, in an analogue manner, multiply–accumulate operations prevalent in artificial neural networks. Various non-volatile memories—including resistive memory 8 – 13 , phase-change memory 14 , 15 and flash memory 16 – 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 – 22 ,  despite the technology’s practical advantages such as endurance and large-scale commercialization 5 . 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. A crossbar array of magnetic memory to execute analogue in-memory computing has been developed, and performs image classification and facial detection at low power.
ISSN:0028-0836
1476-4687
DOI:10.1038/s41586-021-04196-6