Year: 2015
Type: article
Abstract: Memristive devices present a new device technology allowing for the realization of compact non-volatile memories. Some of them are already in the process of industrialization. Additionally, they exhibit complex multilevel and plastic behaviors, which make them good candidates for the implementation of artificial synapses in neuromorphic engineering. However, memristive effects rely on diverse physical mechanisms, and their plastic behaviors differ strongly from one technology to another. Here, we present measurements performed on different memristive devices and the opportunities that they provide. We show that they can be used to implement different learning rules whose properties emerge directly from device physics: real time or accelerated operation, deterministic or stochastic behavior, long term or short term plasticity. We then discuss how such devices might be integrated into a complete architecture. These results highlight that there is no unique way to exploit memristive devices in neuromorphic systems. Understanding and embracing device physics is the key for their optimal use.
Source: Frontiers in Neuroscience
Institutions: Centre National de la Recherche Scientifique, Université de Bordeaux, Laboratoire de l'Intégration du Matériau au Système, University of Zurich, ETH Zurich, Institute of Neuroinformatics, Instituto de Microelectrónica de Sevilla, Universidad de Sevilla, Chemnitz University of Technology, Laboratoire Albert Fert, Université Paris-Sud, Institut d'Electronique, de Microélectronique et de Nanotechnologie, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Laboratoire d'Intégration des Systèmes et des Technologies
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,801
Awards: The Human Brain Project, G265847165, Memristive Hardware Artificial Neural Networks, G3207356259, G393177772, Choreographing neural networks: coupling attractor dynamics and state-dependent computations across biomimetic brain interfaces with neuromorphic VLSI, G459516591, Development and integration of nanoscale memristive devices for neuro-inspired computing systems, G496129201, Bottom-up Approaches to Machines dedicated to Bayesian Inference, G5702163051, G6702880738, Cognitive Systems with Stochastic Spintronics Devices, SYNAPse-like transisTOR and circuits for neuro-inspired computing architecture, Real neurons-nanoelectronics Architecture with Memristive Plasticity, G7754384676, SYnaptic MOlecular NEtworks for Bio-inspired Information Processing, G8702033386
OpenAlex ID: W2083407323