Evidence map›Paper›PMID 39893160›Full record

ArticleNature communications2025

Layer ensemble averaging for fault tolerance in memristive neural networks.

Osama Yousuf, Brian D Hoskins, Karthick Ramu, Mitchell Fream, William A Borders, Advait Madhavan, Matthew W Daniels, Andrew Dienstfrey, Jabez J McClelland, Martin Lueker-Boden and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

11 authors.

Osama YousufDepartment of Electrical and Computer Engineering, George Washington University, Washington, DC, USA.ORCID http://orcid.org/0000-0002-7123-3982
Brian D HoskinsNational Institute of Standards and Technology, Gaithersburg, MD, USA.
Karthick RamuNational Institute of Standards and Technology, Gaithersburg, MD, USA.
Mitchell FreamNational Institute of Standards and Technology, Gaithersburg, MD, USA.ORCID http://orcid.org/0009-0003-1692-9359
William A BordersNational Institute of Standards and Technology, Gaithersburg, MD, USA.ORCID http://orcid.org/0000-0002-0421-5282
Advait MadhavanNational Institute of Standards and Technology, Gaithersburg, MD, USA.
Matthew W DanielsNational Institute of Standards and Technology, Gaithersburg, MD, USA.ORCID http://orcid.org/0000-0002-3390-4714
Andrew DienstfreyNational Institute of Standards and Technology, Boulder, CO, USA.
Jabez J McClellandNational Institute of Standards and Technology, Gaithersburg, MD, USA.ORCID http://orcid.org/0000-0001-5672-5965
Martin Lueker-BodenWestern Digital Technologies, San Jose, CA, USA.ORCID http://orcid.org/0000-0002-4603-7023
Gina C AdamDepartment of Electrical and Computer Engineering, George Washington University, Washington, DC, USA. ginaadam@gwu.edu.ORCID http://orcid.org/0000-0003-0027-1145

Funding

George Washington University (GW) Cross-Disciplinary Research FundNational Science Foundation (NSF) 2239951United States Department of Commerce | National Institute of Standards and Technology (NIST) 70NANB22H018United States Department of Defense | United States Air Force | AFMC | Air Force Office of Scientific Research (AF Office of Scientific Research) FA9550-23-1-0173Western Digital (WD) ECNS21932N
6 · The paper itself

Abstract

Artificial neural networks have advanced due to scaling dimensions, but conventional computing struggles with inefficiencies due to memory bottlenecks. In-memory computing architectures using memristor devices offer promise but face challenges due to hardware non-idealities. This work proposes layer ensemble averaging-a hardware-oriented fault tolerance scheme for improving inference performance of non-ideal memristive neural networks programmed with pre-trained solutions. Simulations on an image classification task and hardware experiments on a continual learning problem with a custom 20,000-device prototyping platform show significant performance gains, outperforming prior methods at similar redundancy levels and overheads. For the image classification task with 20% stuck-at faults, accuracy improves from 40% to 89.6% (within 5% of baseline), and for the continual learning problem, accuracy improves from 55% to 71% (within 1% of baseline). The proposed scheme is broadly applicable to accelerators based on a variety of different non-volatile device technologies.

Identifiers

PMID39893160
PMCPMC11787353

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.