ArticleScientific reports2020
NeuroMem: Analog Graphene-Based Resistive Memory for Artificial Neural Networks.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Optimized capacitance sensor configuration with enhanced sensitivity for annular two-phase flow measurement.Scientific reports · 2025Article
- Photogating Regimes in Graphene: Memory-Bearing and Reset-Free Operation.Nanomaterials (Basel, Switzerland) · 2025Article
- Improved all photonics diffraction neural network based on multi-channel integrated optical fibers.iScience · 2025Article
- Efficient memristor accelerator for transformer self-attention functionality.Scientific reports · 2024Article
- Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise.Journal of clinical medicine · 2023Article
- Graphene-Related Nanomaterials for Biomedical Applications.Nanomaterials (Basel, Switzerland) · 2023Review
- Graphene-based RRAM devices for neural computing.Frontiers in neuroscience · 2023Review
- Artificial Intelligence-Aided Low Cost and Flexible Graphene Oxide-Based Paper Sensor for Ultraviolet and Sunlight Monitoring.Nanoscale research letters · 2022Article
- Tunable Switching Behavior of GO-Based Memristors Using Thermal Reduction.Nanomaterials (Basel, Switzerland) · 2022Article
- Soft and flexible: core-shell ionic liquid resistive memory for electronic synapses.Microsystems & nanoengineering · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence (AI) at the edge has become a hot subject of the recent technology-minded publications. The challenges related to IoT nodes gave rise to research on efficient hardware-based accelerators. In this context, analog memristor devices are crucial elements to efficiently perform the multiply-and-add (MAD) operations found in many AI algorithms. This is due to the ability of memristor devices to perform in-memory-computing (IMC) in a way that mimics the synapses in human brain. Here, we present a novel planar analog memristor, namely NeuroMem, that includes a partially reduced Graphene Oxide (prGO) thin film. The analog and non-volatile resistance switching of NeuroMem enable tuning it to any value within the R
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