ReviewNature communications2024
Hardware implementation of memristor-based artificial neural networks.
Review in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 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
86 citing papers in PubMed.
- Ultralow-Energy Analog Reservoir Computing via Reconfigurable 2-Transistor Memory.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A Dual-Programmable 2M NOR Flash Architecture for Energy-Efficient In-Memory Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Integrating neural decoding, memristive materials, and adaptive control frameworks for next-generation hippocampal memory prosthetics.iScience · 2026Review
- Capacitive in-memory vector-matrix multiplication computing with charge-trap memcapacitors.Nano convergence · 2026Article
- Ultra-low-power probabilistic graphical computing based on memristors enabling communications signal processing.Nature communications · 2026Article
- Spectrally Partitioned All-Optical Memristors for Highly Linear Neuromorphic Vision Systems.Nano-micro letters · 2026Article
- Wafer-scale SOT-MRAM for analog crossbar array applications.Science advances · 2026Article
- Analysis and Applications of Neuromorphic Memristors in Artificial Intelligence Computing.Nano-micro letters · 2026Review
- Neuromorphic Devices and Computing for Sensing, Memory, and Control.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Damage-free van der Waals metal/NbONano convergence · 2026Article
- High-precision time-domain parallelism photonic computing.Science advances · 2026Article
- Integration of Reconfigurable p-Bit and 1R Crossbar Array for Memristive Probabilistic Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Realization of the Bienenstock-Cooper-Munro rule in a single memristor.Nature communications · 2026Article
- Strategies of high-accuracy memristor-based analogue computing in memory for artificial intelligence.Nature materials · 2026Review
- Sustainable Synaptic Device with Two-Dimensional Ferroelectric Materials for Neuromorphic Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Carbon Dot Composite Mixed Ionic-Electronic Device Based on Oxygen-Proton Coupling Catalytic Mechanism.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- New Frontiers in AI-Nano Converged Platforms for Intelligent Diagnostics, Therapeutics, and Safety Evaluation.Chem & bio engineering · 2026Review
- A fully-CMOS spiking LIF neuron implementation for optimized STDP learning on memristor.Scientific reports · 2026Article
- Ultra-high density perovskite nanowire array memristor-based multi-layer perceptron.Nature communications · 2026Article
- Ultrafast switching and high-endurance nonvolatile memory enabled by intrinsic switchable polarization in semiconducting Janus monolayers.Nature communications · 2026Article
26 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
31 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach.
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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.