ArticleNature communications2018
Signal and noise extraction from analog memory elements for neuromorphic computing.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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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.
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Who cites it
14 citing papers in PubMed.
- Atomistic Origin of RTN-like Centers Created and Annihilated by RRAM Write Processes.Nano letters · 2026Article
- WOScientific reports · 2023Article
- Material to system-level benchmarking of CMOS-integrated RRAM with ultra-fast switching for low power on-chip learning.Scientific reports · 2023Article
- Controlling resistive switching behavior in the solution processed SiOScientific reports · 2022Article
- Ion-Movement-Based Synaptic Device for Brain-Inspired Computing.Nanomaterials (Basel, Switzerland) · 2022Review
- Data-driven RRAM device models using Kriging interpolation.Scientific reports · 2022Article
- Article
- Roadmap on emerging hardware and technology for machine learning.Nanotechnology · 2021Article
- On Improving The Computing Capacity of Dynamical Systems.Scientific reports · 2020Article
- Breaking the Quantum PIN Code of Atomic Synapses.Nano letters · 2020Article
- On-Chip Training Spiking Neural Networks Using Approximated Backpropagation With Analog Synaptic Devices.Frontiers in neuroscience · 2020Article
- Versatile stochastic dot product circuits based on nonvolatile memories for high performance neurocomputing and neurooptimization.Nature communications · 2019Article
- Stretchable elastic synaptic transistors for neurologically integrated soft engineering systems.Science advances · 2019Article
- Nanosystems, Edge Computing, and the Next Generation Computing Systems.Sensors (Basel, Switzerland) · 2019Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dense crossbar arrays of non-volatile memory (NVM) can potentially enable massively parallel and highly energy-efficient neuromorphic computing systems. The key requirements for the NVM elements are continuous (analog-like) conductance tuning capability and switching symmetry with acceptable noise levels. However, most NVM devices show non-linear and asymmetric switching behaviors. Such non-linear behaviors render separation of signal and noise extremely difficult with conventional characterization techniques. In this study, we establish a practical methodology based on Gaussian process regression to address this issue. The methodology is agnostic to switching mechanisms and applicable to various NVM devices. We show tradeoff between switching symmetry and signal-to-noise ratio for HfO
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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.