Concept

Memristor

Related publications (176)

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Memory chip or memory array for wide-voltage range in-memory computing using bitline technology

David Atienza Alonso, Marina Zapater Sancho, Marco Antonio Rios, William Andrew Simon

A random access memory having a memory array having a plurality of local memory groups, each local memory group including a plurality of bitcells arranged in a bitcell column, a pair of local bitlines operatively connected to the plurality of bitcells, a p ...

2021

Highly data-centric AI workloads require that new computing paradigms be adopted because the performance of traditional CPU- and GPU-based systems are limited by data access and transfer. Training deep neural networks with millions of tunable parameters ta ...

EPFL2020

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HfO2-based ferroelectrics have dramatically changed the application perspectives of polarization-switching materials for information processing and storage. Their CMOS compatibility and preservation of high reversible polarization down to a few nanometer t ...

AMER INST PHYSICS2020

Accurate Emulation of Memristive Crossbar Arrays for In-Memory Computing

Irem Boybat Kara, Evangelos Eleftheriou

In-memory computing is an emerging non-von Neumann computing paradigm where certain computational tasks are performed in memory by exploiting the physical attributes of the memory devices. Memristive devices such as phase-change memory (PCM), where informa ...

IEEE2020

Accurate deep neural network inference using computational phase-change memory

Irem Boybat Kara, Evangelos Eleftheriou, Abu Sebastian

In-memory computing using resistive memory devices is a promising non-von Neumann approach for making energy-efficient deep learning inference hardware. However, due to device variability and noise, the network needs to be trained in a specific way so that ...

2020

Output-Capacitance Hysteresis Losses of Field-Effect Transistors

Elison de Nazareth Matioli, Armin Jafari, Luca Nela, Palliyage Srilak Nirmana Perera, Georgios Kampitsis

Resonant-type power converters are supposed to generate zero switching losses during soft-switching operation. In recent research, unexpected switching losses were reported in these converters, which were attributed to a large-signal hysteresis observed in ...

IEEE2020

Experimental Demonstration of Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses

Irem Boybat Kara, Evangelos Eleftheriou, Abu Sebastian

Spiking neural networks (SNN) are computational models inspired by the brain's ability to naturally encode and process information in the time domain. The added temporal dimension is believed to render them more computationally efficient than the conventio ...

2020

Permanent photodoping of plasmonic gallium-ZnO nanocrystals

Reinis Ignatans

Donor dopants in oxide semiconductors are compensated not only by valuable electrons but also by other point defects, leading to a decrease in electric conductivity and infrared absorption. We demonstrate that the electron compensation mechanism in Ga dope ...

ROYAL SOC CHEMISTRY2020

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Wide-band-gap technologies enable ultra-high efficiencies in high-frequency power conversion. However, inadequate accuracies in electrical measurements could lead to spurious efficiency measurements, even above 100%. Furthermore, bandwidth limitations, par ...

IEEE2020

The revolution of information-technology owes to silicon-based complementary-metal-oxide (CMOS) technology. However, CMOS technology approaches its physical limitation hardening the further progress of memory devices as well as computing paradigm requiring ...

EPFL2019