ReviewiScience2020
Integration and Co-design of Memristive Devices and Algorithms for Artificial Intelligence.
Review in iScience, 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, 79 citations in OpenAlex.
- Oxide Semiconductor Thin-Film Transistors for Low-Power Electronics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Memristive Ion Dynamics to Enable Biorealistic Computing.Chemical reviews · 2025Review
- Shaping high-performance wearable robots for human motor and sensory reconstruction and enhancement.Nature communications · 2024Review
- Generative complex networks within a dynamic memristor with intrinsic variability.Nature communications · 2023Article
- Short-Term Memory Characteristics of IGZO-Based Three-Terminal Devices.Materials (Basel, Switzerland) · 2023Article
- Recent progress in three-terminal artificial synapses based on 2D materials: from mechanisms to applications.Microsystems & nanoengineering · 2023Review
- A Unified Current-Voltage Model for Metal Oxide-Based Resistive Random-Access Memory.Materials (Basel, Switzerland) · 2022Article
- Compact SPICE Model of Memristor with Barrier Modulated Considering Short- and Long-Term Memory Characteristics by IGZO Oxygen Content.Micromachines · 2022Article
- All-Printed Flexible Memristor with Metal-Non-Metal-Doped TiONanomaterials (Basel, Switzerland) · 2022Article
- Effects of the Operating Ambiance and Active Layer Treatments on the Performance of Magnesium Fluoride Based Bipolar RRAM.Nanomaterials (Basel, Switzerland) · 2022Article
- Memristor-CMOS Hybrid Neuron Circuit with Nonideal-Effect Correction Related to Parasitic Resistance for Binary-Memristor-Crossbar Neural Networks.Micromachines · 2021Article
- Article
- Adaptive Extreme Edge Computing for Wearable Devices.Frontiers in neuroscience · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Memristive devices share remarkable similarities to biological synapses, dendrites, and neurons at both the physical mechanism level and unit functionality level, making the memristive approach to neuromorphic computing a promising technology for future artificial intelligence. However, these similarities do not directly transfer to the success of efficient computation without device and algorithm co-designs and optimizations. Contemporary deep learning algorithms demand the memristive artificial synapses to ideally possess analog weighting and linear weight-update behavior, requiring substantial device-level and circuit-level optimization. Such co-design and optimization have been the main focus of memristive neuromorphic engineering, which often abandons the "non-ideal" behaviors of memristive devices, although many of them resemble what have been observed in biological components. Novel brain-inspired algorithms are being proposed to utilize such behaviors as unique features to further enhance the efficiency and intelligence of neuromorphic computing, which calls for collaborations among electrical engineers, computing scientists, and neuroscientists.
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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.