Evidence map›Paper›PMID 41530174›Full record

ArticleNature communications2026

Domain wall motion-driven magnetic convolutional accelerator.

Bingqian Dai, Tianyi Wang, Albert Lee, Shijie Xu, Chin-Chung Chen, Kin Wong, Dingyi Li, Malcolm Jackson, Yang Cheng, Puyang Huang and 9 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Bingqian Dai *Department of Electrical and Computer Engineering, Physics and Astronomy, and Material Science and Engineering, University of California, Los Angeles, CA, USA. bdai@g.ucla.edu.ORCID http://orcid.org/0000-0001-9904-4078
Tianyi Wang *Department of Electrical and Computer Engineering, Physics and Astronomy, and Material Science and Engineering, University of California, Los Angeles, CA, USA.ORCID http://orcid.org/0009-0008-4110-075X
Albert Lee *Department of Electrical and Computer Engineering, Physics and Astronomy, and Material Science and Engineering, University of California, Los Angeles, CA, USA.
Shijie Xu *Department of Electrical and Computer Engineering, Physics and Astronomy, and Material Science and Engineering, University of California, Los Angeles, CA, USA.
Chin-Chung ChenDepartment of Electrical and Computer Engineering, Physics and Astronomy, and Material Science and Engineering, University of California, Los Angeles, CA, USA.
Kin WongDepartment of Electrical and Computer Engineering, Physics and Astronomy, and Material Science and Engineering, University of California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0001-6776-3852
Dingyi LiDepartment of Electrical and Computer Engineering, Physics and Astronomy, and Material Science and Engineering, University of California, Los Angeles, CA, USA.
Malcolm JacksonDepartment of Electrical and Computer Engineering, Physics and Astronomy, and Material Science and Engineering, University of California, Los Angeles, CA, USA.
Yang ChengDepartment of Electrical and Computer Engineering, Physics and Astronomy, and Material Science and Engineering, University of California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0002-9181-5314
Puyang HuangDepartment of Electrical and Computer Engineering, Physics and Astronomy, and Material Science and Engineering, University of California, Los Angeles, CA, USA.
Yaochen LiDepartment of Electrical and Computer Engineering, Physics and Astronomy, and Material Science and Engineering, University of California, Los Angeles, CA, USA.
Chao YunSchool of Materials, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China.
Qingyuan ShuDepartment of Electrical and Computer Engineering, Physics and Astronomy, and Material Science and Engineering, University of California, Los Angeles, CA, USA.
Haoran HeDepartment of Electrical and Computer Engineering, Physics and Astronomy, and Material Science and Engineering, University of California, Los Angeles, CA, USA.
Lixuan TaiDepartment of Electrical and Computer Engineering, Physics and Astronomy, and Material Science and Engineering, University of California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0001-5144-7337
Hanshen HuangDepartment of Electrical and Computer Engineering, Physics and Astronomy, and Material Science and Engineering, University of California, Los Angeles, CA, USA.
Tien-Kan ChungDepartment of Mechanical Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Yanglong HouSchool of Materials, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China. hou@sysu.edu.cn.
Kang L WangDepartment of Electrical and Computer Engineering, Physics and Astronomy, and Material Science and Engineering, University of California, Los Angeles, CA, USA. wang@ee.ucla.edu.ORCID http://orcid.org/0000-0002-9363-1279

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Modern computing powers applications from data analysis to artificial intelligence but now faces limitations. The slowdown of device scaling and the bottleneck between memory and processors motivate architectures that unify computation and data storage. Convolution is a core operation in learning, vision, and signal processing, yet its conventional implementation incurs high energy, high latency, and limited scalability. Magnetic systems that host spin textures, such as domain walls, offer dynamic behaviors that enable computation beyond traditional logic. Here we introduce a compute-in-memory platform that performs convolution by sequentially shifting magnetic domains and sensing the resulting signals. Information is written directly into domain patterns, processed through controlled motion, and read electrically, forming a nonvolatile structure suited for convolution tasks. This approach supports applications including Fourier analysis, neural networks, and image processing, achieving 10

Identifiers

PMID41530174
PMCPMC12868888

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.