ArticleScience advances2020
One-step regression and classification with cross-point resistive memory arrays.
Article in Science advances, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Article
- Resistive memory-based neural differential equation solver for score-based diffusion model.Nature communications · 2026Article
- Memristive crossbar array-based hardware framework for compressed sensing and event-driven neuromorphic processing.Nature communications · 2026Article
- Privacy-preserving data analysis using a memristor chip with colocated authentication and processing.Science advances · 2026Article
- In-memory analog computing for non-negative matrix factorization.Nature communications · 2026Article
- Achieving high precision in analog in-memory computing systems.Npj unconventional computing · 2026Review
- Resistive Switching Random-Access Memory (RRAM): Applications and Requirements for Memory and Computing.Chemical reviews · 2025Review
- In-memory analog solution of compressed sensing recovery in one step.Science advances · 2023Article
- Toward memristive in-memory computing: principles and applications.Frontiers of optoelectronics · 2022Review
- Article
- Multimodal transistors as ReLU activation functions in physical neural network classifiers.Scientific reports · 2022Article
- Memristive and CMOS Devices for Neuromorphic Computing.Materials (Basel, Switzerland) · 2020Review
- Neurohybrid Memristive CMOS-Integrated Systems for Biosensors and Neuroprosthetics.Frontiers in neuroscience · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning has been getting attention in recent years as a tool to process big data generated by the ubiquitous sensors used in daily life. High-speed, low-energy computing machines are in demand to enable real-time artificial intelligence processing of such data. These requirements challenge the current metal-oxide-semiconductor technology, which is limited by Moore's law approaching its end and the communication bottleneck in conventional computing architecture. Novel computing concepts, architectures, and devices are thus strongly needed to accelerate data-intensive applications. Here, we show that a cross-point resistive memory circuit with feedback configuration can train traditional machine learning algorithms such as linear regression and logistic regression in just one step by computing the pseudoinverse matrix of the data within the memory. One-step learning is further supported by simulations of the prediction of housing price in Boston and the training of a two-layer neural network for MNIST digit recognition.
Identifiers
What OpenQuestion holds
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.