ArticleNature nanotechnology2021
Scalable massively parallel computing using continuous-time data representation in nanoscale crossbar array.
Article in Nature nanotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Artificial sparse neuron dendrites for visual information inference.Science advances · 2026Article
- Massively parallel in-sensor skinomorphic computing.Nature communications · 2026Article
- Ferroelectricity-modulated asymmetric van der Waals heterostructure for ultralow-power neuromorphic synapse and logic-in-memory operations.Nature communications · 2026Article
- Multisensory Neuromorphic Devices: From Physics to Integration.Nano-micro letters · 2026Review
- Multimodal In-Sensor Computing with Dual-Phase Organic Synapses for Wearable Fitness Monitoring.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Interstitial nanofilament pathway reduces energy consumption in memristors.Nature nanotechnology · 2025Article
- Molecular crystal memristors.Nature nanotechnology · 2025Article
- Ultrahigh-precision analog computing using memory-switching geometric ratio of transistors.Science advances · 2025Article
- Fully memristive spiking neural network for energy-efficient graph learning.Science advances · 2025Article
- Mortise-tenon-shaped memristors for scientific computing.Science advances · 2025Article
- Memristor-based feature learning for pattern classification.Nature communications · 2025Article
- Enhancing 2D Photonics and Optoelectronics with Artificial Microstructures.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- A low-power vertical dual-gate neurotransistor with short-term memory for high energy-efficient neuromorphic computing.Nature communications · 2023Article
- Thermally stable threshold selector based on CuAg alloy for energy-efficient memory and neuromorphic computing applications.Nature communications · 2023Article
- In-memory photonic dot-product engine with electrically programmable weight banks.Nature communications · 2023Article
- Memristor-Based Signal Processing for Compressed Sensing.Nanomaterials (Basel, Switzerland) · 2023Review
- All-in-One Compression and Encryption Engine Based on Flexible Polyimide Memristor.Small science · 2023Article
- Graphene/MoSLight, science & applications · 2023Article
- Toward memristive in-memory computing: principles and applications.Frontiers of optoelectronics · 2022Review
- Technology agnostic frequency characterization methodology for memristors.Scientific reports · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The growth of connected intelligent devices in the Internet of Things has created a pressing need for real-time processing and understanding of large volumes of analogue data. The difficulty in boosting the computing speed renders digital computing unable to meet the demand for processing analogue information that is intrinsically continuous in magnitude and time. By utilizing a continuous data representation in a nanoscale crossbar array, parallel computing can be implemented for the direct processing of analogue information in real time. Here, we propose a scalable massively parallel computing scheme by exploiting a continuous-time data representation and frequency multiplexing in a nanoscale crossbar array. This computing scheme enables the parallel reading of stored data and the one-shot operation of matrix-matrix multiplications in the crossbar array. Furthermore, we achieve the one-shot recognition of 16 letter images based on two physically interconnected crossbar arrays and demonstrate that the processing and modulation of analogue information can be simultaneously performed in a memristive crossbar array.
Identifiers
34239120What 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.