Evidence map›Paper›PMID 41124260›Full record

ArticleScience advances2025

An overhead-reduced, efficient, fully analog neural-network computing hardware.

Jiabao Ye, Wannian Wang, Caiping Shi, Xuecheng Cui, Wanyuan Qu, Peng Lin, Xiao Yu, Ran Cheng, Hanming Wu, Bing Chen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Science advances, 2025. 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

10 authors.

Jiabao YeCollege of Integrated Circuits, Zhejiang University, Hangzhou 310058, China.ORCID 0009-0009-3406-6714
Wannian WangCollege of Integrated Circuits, Zhejiang University, Hangzhou 310058, China.
Caiping ShiCollege of Integrated Circuits, Zhejiang University, Hangzhou 310058, China.
Xuecheng CuiCollege of Integrated Circuits, Zhejiang University, Hangzhou 310058, China.
Wanyuan QuCollege of Integrated Circuits, Zhejiang University, Hangzhou 310058, China.
Peng LinCollege of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China.ORCID 0000-0002-0679-8063
Xiao YuHangzhou Research Institute, Xidian University, Hangzhou 311231, China.ORCID 0000-0001-8769-521X
Ran ChengCollege of Integrated Circuits, Zhejiang University, Hangzhou 310058, China.
Hanming WuCollege of Integrated Circuits, Zhejiang University, Hangzhou 310058, China.
Bing ChenCollege of Integrated Circuits, Zhejiang University, Hangzhou 310058, China.ORCID 0000-0001-5284-8618

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human brain-inspired neural-network computing hardware has the potential to overcome the energy efficiency and resource overhead challenges caused by the von Neumann bottleneck. However, existing neural-network computing hardware still exhibits a considerable gap compared to the human brain, likely due to the absence of complex data compression functions and the fully analog domain operating strategy adopted by the brain. To solve these problems, a fully analog neural

Identifiers

PMID41124260
PMCPMC12542939

What OpenQuestion holds

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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.