Evidence map›Paper›PMID 41857045›Full record

ArticleNature communications2026

Data-In-situ Computing with One-Pixel-Multiple-Memristor Architecture for Neuromorphic Sequential Vision.

Yi Sun, Peiwen Tong, Jiangrong Shen, Hui Xu, Rongrong Cao, Chang Liu, Changlin Chen, Bing Song, Yinan Wang, Wei Wang and 2 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

12 authors.

Yi Sun *College of Electronic Science and Technology, National University of Defense Technology, Changsha, 410073, China.
Peiwen Tong *College of Electronic Science and Technology, National University of Defense Technology, Changsha, 410073, China.
Jiangrong Shen *Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.
Hui XuCollege of Electronic Science and Technology, National University of Defense Technology, Changsha, 410073, China.
Rongrong CaoCollege of Electronic Science and Technology, National University of Defense Technology, Changsha, 410073, China.ORCID http://orcid.org/0000-0001-8760-8577
Chang LiuCollege of Electronic Science and Technology, National University of Defense Technology, Changsha, 410073, China.
Changlin ChenCollege of Electronic Science and Technology, National University of Defense Technology, Changsha, 410073, China.
Bing SongCollege of Electronic Science and Technology, National University of Defense Technology, Changsha, 410073, China.
Yinan WangCollege of Electronic Science and Technology, National University of Defense Technology, Changsha, 410073, China.
Wei WangCollege of Electronic Science and Technology, National University of Defense Technology, Changsha, 410073, China. wangwei_esss@nudt.edu.cn.ORCID http://orcid.org/0000-0002-7180-831X
Yuchao YangGuangdong Provincial Key Laboratory of In-Memory Computing Chips, School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University, Shenzhen, 518055, China. yuchaoyang@pku.edu.cn.ORCID http://orcid.org/0000-0003-4674-4059
Qingjiang LiCollege of Electronic Science and Technology, National University of Defense Technology, Changsha, 410073, China. qingjiangli@nudt.edu.cn.ORCID http://orcid.org/0000-0001-9779-3198

Funding

National Natural Science Foundation of China (National Science Foundation of China) 62304254National Natural Science Foundation of China (National Science Foundation of China) 62404253National Natural Science Foundation of China (National Science Foundation of China) U23A20322
6 · The paper itself

Abstract

Neuromorphic vision systems based on memristors offer an energy-efficient approach to artificial vision, yet traditional pixel(s)-to-one-memristor architectures remain inefficient in dynamic image processing due to limited temporary storage. Here, inspired by human visual working memory, we propose a one-pixel-multiple-memristor (1PnR) architecture with a rolling exposure strategy for fast sequential image acquisition. Furthermore, a data-in-situ computing network for efficient image processing is developed. With network weights mapped to voltage vectors and applied to the image storage memristor array, direct computation is enabled where the image is stored, and the energy-intensive data transmission is eliminated. A hardware prototype of the 1PnR architecture achieved 95.7% recognition accuracy on the Weizmann human action flow dataset. Compared to CMOS-based systems, this architecture is estimated to have a 2000× reduction in latency for image sensing and storage, and a 160× reduction in energy consumption image processing, demonstrating significant potential for future neuromorphic visual systems.

Identifiers

PMID41857045
PMCPMC13168392

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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.