ArticleNano letters2024
Analog Resistive Switching Devices for Training Deep Neural Networks with the Novel Tiki-Taka Algorithm.
Article in Nano letters, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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.
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Who cites it
6 citing papers in PubMed.
- Implementation of offset corrected AGAD algorithm on 130-nm CMOS technology-based RRAM array for analog neural network training.Science advances · 2026Article
- Update Disturbance-Resilient Analog ReRAM Crossbar Arrays for In-Memory Deep Learning Accelerators.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Red-billed blue magpie optimization for training feedforward neural networks.Scientific reports · 2025Article
- Resistive Switching Random-Access Memory (RRAM): Applications and Requirements for Memory and Computing.Chemical reviews · 2025Review
- Fast and robust analog in-memory deep neural network training.Nature communications · 2024Article
- Analytical modelling of the transport in analog filamentary conductive-metal-oxide/HfONanoscale horizons · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A critical bottleneck for the training of large neural networks (NNs) is communication with off-chip memory. A promising mitigation effort consists of integrating crossbar arrays of analogue memories in the Back-End-Of-Line, to store the NN parameters and efficiently perform the required synaptic operations. The "
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Registered trials
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.