ReviewFrontiers of optoelectronics2022
Toward memristive in-memory computing: principles and applications.
Review in Frontiers of optoelectronics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Research on Memristors in Hardware Security.Micromachines · 2026Review
- Very-large-scale mimetic optogenetic synapses for physical reservoir computing.Nature communications · 2026Article
- Review
- A Review of Nanowire Devices Applied in Simulating Neuromorphic Computing.Nanomaterials (Basel, Switzerland) · 2025Review
- IMPACT: In-Memory ComPuting Architecture based on Y-FlAsh Technology for Coalesced Tsetlin machine inference.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Multilevel Optical Storage, Dynamic Light Modulation, and Polarization Control in Filamented Memristor System.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- MEMS Oscillators-Network-Based Ising Machine with Grouping Method.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Nonvolatile Memristive Materials and Physical Modeling for In-Memory and In-Sensor Computing.Small science · 2024Article
- The Roadmap of 2D Materials and Devices Toward Chips.Nano-micro letters · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the rapid growth of computer science and big data, the traditional von Neumann architecture suffers the aggravating data communication costs due to the separated structure of the processing units and memories. Memristive in-memory computing paradigm is considered as a prominent candidate to address these issues, and plentiful applications have been demonstrated and verified. These applications can be broadly categorized into two major types: soft computing that can tolerant uncertain and imprecise results, and hard computing that emphasizes explicit and precise numerical results for each task, leading to different requirements on the computational accuracies and the corresponding hardware solutions. In this review, we conduct a thorough survey of the recent advances of memristive in-memory computing applications, both on the soft computing type that focuses on artificial neural networks and other machine learning algorithms, and the hard computing type that includes scientific computing and digital image processing. At the end of the review, we discuss the remaining challenges and future opportunities of memristive in-memory computing in the incoming Artificial Intelligence of Things era.
Indexed as
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.