Data Clustering using Memristor Networks
Open accessAbstract
Memristors have emerged as a promising candidate for critical applications such as non-volatile memory as well as non-Von Neumann computing architectures based on neuromorphic and machine learning systems. In this study, we demonstrate that memristors can be used to perform principal component analysis (PCA), an important technique for machine learning and data feature learning. The conductance changes of memristors in response to voltage pulses are studied and modeled with an internal state variable to trace the analog behavior of the device. Unsupervised, online learning is achieved in a memristor crossbar using Sanger's learning rule, a derivative of Hebb's rule, to obtain the principal components. The details of weights evolution during training is investigated over learning epochs as a function of training parameters. The effects of device non-uniformity on the PCA network performance are further analyzed. We show that the memristor-based PCA network is capable of linearly separating distinct classes from sensory data with high clarification success of 97.6% even in the presence of large device variations.
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Most of the literature claiming unsupervised learning with memristive devices used software.
“most of the literature claiming unsupervised learning with memristive devices used software166”
From Hardware implementation of memristor-based artificial neural networks (2024) · cites this paper as [166]
The Methods > Simulation section says this paper used Python for memristor-network simulations, and the Discussion describes its unsupervised-learning analysis. That supports this paper as one example of software-based work, but the source does not assess the wider literature or establish that most such work used software. No page numbers are available in our copy. (LLM, conf=0.78)
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Design of resistive random access memory cell and its architecture
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Hybrid CMOS/memristor crossbar structure for implementing hopfield neural network
Molahasani, M., Shamsi, J., & Shokouhi, S. B. (2020) · Analog Integrated Circuits and Signal Processing · doi:10.1007/s10470-020-01720-y · Cited by 14 · see citing works
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Experimentally calibrated electro-thermal modeling of temperature dynamics in memristors
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Effect of ZnS and PbS shell on mem-behavior of CdS quantum dots
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Self-citationDesign of CNFET based power- and variability-aware nonvolatile RRAM cell
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Cycle-to-cycle Variation Enabled Energy Efficient Privacy Preserving Technology in ANN
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Thermodynamics of self-oscillations in VO2 for spiking solid-state neurons
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A Hybrid Weight Quantization Strategy for Memristive Neural Networks
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ROA: A Rapid Learning Scheme for In-Situ Memristor Networks
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Memristive devices based hardware for unlabeled data processing
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Memristor-CMOS Analog Coprocessor for Acceleration of High-Performance Computing Applications
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Modeling and characterization of stochastic resistive switching in single Ag2S nanowires
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Liquid ferrofluid synapses for spike-based neuromorphic learning
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Effect of Device Mismatches in Differential Oscillatory Neural Networks
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Automatic Learning Rate Adaption for Memristive Deep Learning Systems
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Pulse-Programmed Short-Term Plasticity and Long-Term Potentiation of MoS2 Memristive Devices
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Visualizing the Local Composition Changes during Resistive Switching in Planar TaOx-ReRAMs
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Low Power Memristor Crossbar Based Winner Takes All Circuit
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TCAmMCogniGron: Energy Efficient Memristor-Based TCAM for Match-Action Processing
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Vector multiplications using memristive devices and applications thereof
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Self-citation
- Authors
- Choi, S., Sheridan, P., & Lu, W. D.
- Affiliations
- University of Michigan (US)
- Corresponding author
- Lu, Wei D.
- Venue
- Scientific Reports
- Published
- 2015-05-28
- Volume
- 5
- Issue
- 1
- Pages
- 10492-10492
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC4650685
- OpenAlex
- W368005490
- Cited by
- 132
- References
- 23
- Citations by year
- 2025: 3 · 2024: 11 · 2023: 13 · 2022: 13 · 2021: 16 · 2020: 19 · 2019: 16 · 2018: 18 · 2017: 12 · 2016: 10 · 2015: 1
- FWCI
- 6.58
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- U.S. Department of Defense (FA9550)
- University of Michigan
- Defense Advanced Research Projects Agency (HR0011, HR0011-13-2-0015)
- Samsung
- Multidisciplinary University Research Initiative
- Air Force Office of Scientific Research (FA9550-, FA9550-12, FA9550‐12‐1‐0038, FA9550‐12‐1)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Reservoir Computing
- Keywords
- memristors, principal component analysis, memristor crossbar, Hebbian learning, neuromorphic computing, linear classification, feature extraction
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