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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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Published in: | Nature (London) 2022-01, Vol.601 (7892), p.211-216 |
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Main Authors: | , , , , , , , , , , , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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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. |
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ISSN: | 0028-0836 1476-4687 |
DOI: | 10.1038/s41586-021-04196-6 |