An memristor-based synapse implementation using BCM learning rule [PDF]
, 2021 A novel memristive synapse model based on the HP memristor is proposed in this paper, which can address the problem of synaptic weight infinite modulations.
Harkin, Jim +4 more
core +2 more sources
Compact Modeling Solutions for Oxide-Based Resistive Switching Memories (OxRAM)
Journal of Low Power Electronics and Applications, 2014 Emerging non-volatile memories based on resistive switching mechanisms attract intense R&D efforts from both academia and industry. Oxide-based Resistive Random Acces Memories (OxRAM) gather noteworthy performances, such as fast write/read speed, low
Marc Bocquet +9 more
doaj +1 more source
Pavlov's dog associative learning demonstrated on synaptic-like organic transistors [PDF]
, 2013 In this letter, we present an original demonstration of an associative learning neural network inspired by the famous Pavlov's dogs experiment. A single nanoparticle organic memory field effect transistor (NOMFET) is used to implement each synapse.
Alibart, F. +6 more
core +3 more sources
Ultra-High-density 3D vertical RRAM with stacked JunctionLess nanowires for In-Memory-Computing applications [PDF]
, 2020 The Von-Neumann bottleneck is a clear limitation for data-intensive applications, bringing in-memory computing (IMC) solutions to the fore. Since large data sets are usually stored in nonvolatile memory (NVM), various solutions have been proposed based ...
Andrieu, F. +8 more
core +1 more source
Performance and reliability comparison of 1T-1R RRAM arrays with amorphous and polycrystalline HfO2 [PDF]
, 2016 In this work, a comparison between 1T-1R RRAM 4kbits arrays manufactured either with amorphous or polycrystalline HfO2 in terms of performance, reliability, Set/Reset operations energy requirements, intra-cell and inter-cell variability during 10k Set ...
Grossi, Alessandro +4 more
core +1 more source
Exploiting Inter- and Intra-Memory Asymmetries for Data Mapping in Hybrid Tiered-Memories
, 2020 Modern computing systems are embracing hybrid memory comprising of DRAM and non-volatile memory (NVM) to combine the best properties of both memory technologies, achieving low latency, high reliability, and high density.
Antognetti P. +32 more
core +1 more source
A differential memristive synapse circuit for on-line learning in neuromorphic computing systems [PDF]
, 2017 Spike-based learning with memristive devices in neuromorphic computing architectures typically uses learning circuits that require overlapping pulses from pre- and post-synaptic nodes.
Indiveri, Giacomo +2 more
core +1 more source
Electrical characterization and modeling of 1T-1R RRAM arrays with amorphous and poly-crystalline HfO2 [PDF]
, 2017 In this work, a comparison between 1T-1R RRAM arrays, manufactured either with amorphous or poly-crystalline Metal–Insulator–Metal cells, is reported in terms of performance, reliability, Set/Reset operations energy requirements, intra-cell and inter ...
Crespo Yepes, Alberto +8 more
core +1 more source
Conversion of a digital camera into a non-contact colorimeter for use in stone cultural heritage: The application case to Spanish granites [PDF]
, 2014 In this study, a digital CMOS camera was calibrated for use as a non-contact colorimeter for measuring the color of granite artworks. The low chroma values of the granite, which yield similar stimulation of the three color channels of the camera, proved ...
Chorro, Elísabet +4 more
core +2 more sources
Synaptic metaplasticity with multi-level memristive devices [PDF]
, 2023 Deep learning has made remarkable progress in various tasks, surpassing human performance in some cases. However, one drawback of neural networks is catastrophic forgetting, where a network trained on one task forgets the solution when learning a new one.
Arcamone, J +7 more
core +1 more source