ReviewNature nanotechnology2020
Memory devices and applications for in-memory computing.
Review in Nature nanotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 303 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
303 citing papers in PubMed.
- Lattice Disorder in CuI Enables Filament-Free Transparent Memristors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Deterministic Switching-Path Engineering of CMOS-Integrated 2D Memristors for Neuromorphic Computing.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Unveiling Search-Robust One-Shot Learning Operation in FeTFT-Based Ternary Content-Addressable Memory.Small science · 2026Article
- Understanding and Designing Phase Change Materials: Insights From Atom Probe Tomography.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Opposite Trap-State Evolution Pathways Govern Endurance and Retention Failure in Amorphous Chalcogenide 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
- High-precision hyperdimensional computing enabled by in-memory computing using high-polarization ferroelectric HfMicrosystems & nanoengineering · 2026Article
- Hardware-Algorithm Co-Optimization of Weight-Update Protocols in Oxide-Based Synaptic Transistor Arrays for Neuromorphic Systems.Micromachines · 2026Article
- Defect-Engineered Floating-Gate Synapses With Programmable Relaxation Dynamics for Multi-Timescale Neuromorphic Computing.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Wafer-Scale 3D Integration for High-Density Multi-Valued Neuromorphic Logic.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Triple-Level Content-Addressable Vertical NAND Flash Memory.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Two-Dimensional Indium Selenide for Next-Generation Electronics.International journal of molecular sciences · 2026Review
- Analysis and Applications of Neuromorphic Memristors in Artificial Intelligence Computing.Nano-micro letters · 2026Review
- Damage-free van der Waals metal/NbONano convergence · 2026Article
- Interfacial Engineering of MoSNanomaterials (Basel, Switzerland) · 2026Article
- A Sustainable Organic Memristor with Tunable Switching for Logic Operations and Medical Image Encryption.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Voltage-Programmable Switching in Halide Perovskite Memristors by Asymmetric Contact Engineering.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Adaptive Redox Resistive Memory Programming for Efficient and Robust Class-Incremental Learning.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Hysteresis Inversion Driven by Polar-Order Competition in van der Waals Ferrielectric.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Charge-Encoded Sidechains Enable Deterministic Ion Ingress and Memory Retention in Organic Electrochemical Synaptic Transistors.Advanced materials (Deerfield Beach, Fla.) · 2026Article
243 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traditional von Neumann computing systems involve separate processing and memory units. However, data movement is costly in terms of time and energy and this problem is aggravated by the recent explosive growth in highly data-centric applications related to artificial intelligence. This calls for a radical departure from the traditional systems and one such non-von Neumann computational approach is in-memory computing. Hereby certain computational tasks are performed in place in the memory itself by exploiting the physical attributes of the memory devices. Both charge-based and resistance-based memory devices are being explored for in-memory computing. In this Review, we provide a broad overview of the key computational primitives enabled by these memory devices as well as their applications spanning scientific computing, signal processing, optimization, machine learning, deep learning and stochastic computing.
Identifiers
32231270What 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.