Evidence map›Paper›PMID 40673873›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025

Memristive In-Memory Object Detection with 128 Mb C-Doped Ge

Chenchen Xie, Yuqi Li, Longhao Yan, Sannian Song, Houpeng Chen, Ruijuan Qi, Xi Li, Yihang Zhu, Lianfeng Yu, Bonan Yan and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 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. Review
  2. Memristive In-Memory Object Detection with 128 Mb C-Doped GeAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    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

14 authors.

Chenchen XieState Key Laboratory of Functional Materials for Informatics, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China.
Yuqi LiBeijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China.
Longhao YanBeijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China.
Sannian SongState Key Laboratory of Functional Materials for Informatics, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China.
Houpeng ChenState Key Laboratory of Functional Materials for Informatics, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China.
Ruijuan QiState Key Laboratory of Functional Materials for Informatics, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China.
Xi LiState Key Laboratory of Functional Materials for Informatics, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China.
Yihang ZhuBeijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China.
Lianfeng YuBeijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China.
Bonan YanBeijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China.
Yaoyu TaoBeijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China.
Gaoming FengShanghai Integrated Circuit R&D Center, Shanghai, 201210, China.
Yuchao YangBeijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China.ORCID https://orcid.org/0000-0003-4674-4059
Zhitang SongState Key Laboratory of Functional Materials for Informatics, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China.

Funding

Beijing Natural Science Foundation 25D40029Beijing Natural Science Foundation 25FY3314Beijing Natural Science Foundation L234026Guangdong Provincial Key Laboratory of In-Memory Computing Chips 2024B1212020002National Natural Science Foundation of China 62404004National Natural Science Foundation of China 8206100486National Natural Science Foundation of China 92164302Shenzhen Science and Technology Program JCYJ20241202125907011the 111 Project B18001
6 · The paper itself

Abstract

Object detection, as a fundamental task in computer vision, mainly performs the classification and localization of objects in images or videos. However, traditional edge computing platforms fall short of meeting the demands for state-of-the-art object detection model size and computing power. Here, a 128 Mb phase change memory chip is fabricated with a high memory yield of 99.99999% in a 40 nm node and utilized for efficient in-memory vector-matrix multiplication and in-memory max computation. In particular, in order to mitigate the significant programming energy overheads for large-scale memristor arrays and the reliance on high-precision analog-to-digital-converter (ADC) in compute-in-memory operations, a novel mixed-precision weight mapping strategy is adopted. Compared with traditional schemes, the ADC modules achieve up to a 22.3× reduction in energy consumption while maintaining equivalent network performance. Ultimately, this memristive in-memory object detection system demonstrates 4,180× higher energy efficiency and 228× greater computational throughput compared to GPU implementations.

Indexed as

in‐memory computingmixed precisionobject detectionphase change memory

Identifiers

PMID40673873
PMCPMC12463051

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.