Mémoire à changement de phase

Résumé

La mémoire à changement de phase, ou PCM pour Phase Change Memory, ou encore PRAM pour Phase-Change RAM, est un type de mémoire non volatile s'appuyant sur la transition de phase de certains matériaux pour le stockage des informations, à l'instar des disques optiques réinscriptibles. Elle est présentée comme mémoire universelle car elle combine la vitesse et l’endurance de la mémoire vive (RAM) et la non-volatilité et le faible coût de la mémoire flash. Les premières PRAM ont été mises en vente en 2012 par Samsung. Historique À la fin des années 1960, les travaux menés par Stanford R. Ovshinsky aux laboratoires conduisent à la découverte d'une transition de phase rapide et réversible de différents types de matériau semi-conducteur effectuée avec un courant électrique. Description La PCM utilise la propriété des matériaux à changement de phase, tel que le verre de chalcogé

Source officielle

https://fr.wikipedia.org/wiki/M%C3%A9moire_%C3%A0_changement_de_phase

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Learning spatio-temporal patterns in the presence of input noise using phase-change memristors

Evangelos Eleftheriou, Angeliki Pantazi, Stanislaw Andrzej Wozniak

Neuromorphic systems increasingly attract research interest owing to their ability to provide biologically inspired methods of computing, alternative to the classic von Neumann architecture. In these systems, computing relies on spike-based communication between neurons, and memory is represented by evolving states of the synaptic interconnections. In this work, we first demonstrate how spike-timing-dependent plasticity (STDP) based synapses can be realized using the crystal-growth dynamics of phase-change memristors. Then, we present a novel learning architecture comprising an integrate-and-fire neuron and an array of phase-change synapses that is capable of detecting temporal correlations in parallel input streams. We demonstrate a continuous re-learning operation on a sequence of binary 20×20 pixel images in the presence of significant background noise. Experimental results using an array of phase-change cells as synaptic elements confirm the functionality and performance of the proposed learning architecture.

2016

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All-memristive neuromorphic computing with level-tuned neurons

Evangelos Eleftheriou, Angeliki Pantazi, Stanislaw Andrzej Wozniak

In the new era of cognitive computing, systems will be able to learn and interact with the environment in ways that will drastically enhance the capabilities of current processors, especially in extracting knowledge from vast amount of data obtained from many sources. Brain-inspired neuromorphic computing systems increasingly attract research interest as an alternative to the classical von Neumann processor architecture, mainly because of the coexistence of memory and processing units. In these systems, the basic components are neurons interconnected by synapses. The neurons, based on their nonlinear dynamics, generate spikes that provide the main communication mechanism. The computational tasks are distributed across the neural network, where synapses implement both the memory and the computational units, by means of learning mechanisms such as spike-timing-dependent plasticity. In this work, we present an all-memristive neuromorphic architecture comprising neurons and synapses realized by using the physical properties and state dynamics of phase-change memristors. The architecture employs a novel concept of interconnecting the neurons in the same layer, resulting in level-tuned neuronal characteristics that preferentially process input information. We demonstrate the proposed architecture in the tasks of unsupervised learning and detection of multiple temporal correlations in parallel input streams. The efficiency of the neuromorphic architecture along with the homogenous neuro-synaptic dynamics implemented with nanoscale phase-change memristors represent a significant step towards the development of ultrahigh-density neuromorphic co-processors.

Iop Publishing Ltd2016

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Hardware accelerators based on two-terminal non-volatile memories (NVMs) can potentially provide competitive speed and accuracy for the training of fully connected deep neural networks (FC-DNNs), with respect to GPUs and other digital accelerators. We recently proposed [S. Ambrogio et al., Nature, 2018] novel neuromorphic crossbar arrays, consisting of a pair of phase-change memory (PCM) devices combined with a pair of 3-Transistor 1-Capacitor (3T1C) circuit elements, so that each weight was implemented using multiple conductances of varying significance, and then showed that this weight element can train FC-DNNs to software-equivalent accuracies. Unfortunately, however, real arrays of emerging NVMs such as PCM typically include some failed devices (e.g.,

2019

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