On the application of a diffusive memristor compact model to neuromorphic circuits

Memristive devices have found application in both random access memory and neuromorphic circuits. In particular, it is known that their behavior resembles that of neuronal synapses. However, it is not simple to come by samples of memristors and adjusting their parameters to change their response req...

Descripción completa

Detalles Bibliográficos
Autores: Cisternas Ferri, Agustín, Rapoport, Alan, Fierens, Pablo I.|||0000-0001-5725-0017, Patterson, German A.|||0000-0001-9035-3042, Miranda, E.|||0000-0003-0470-5318, Suñé, Jordi|||0000-0003-0108-4907
Tipo de recurso: artículo
Fecha de publicación:2019
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:215755
Acceso en línea:https://ddd.uab.cat/record/215755
https://dx.doi.org/urn:doi:10.3390/ma12142260
Access Level:acceso abierto
Palabra clave:Memristor
Compact model
Emulator
Neuromorphic
Synapse
STDP
Pavlov
Descripción
Sumario:Memristive devices have found application in both random access memory and neuromorphic circuits. In particular, it is known that their behavior resembles that of neuronal synapses. However, it is not simple to come by samples of memristors and adjusting their parameters to change their response requires a laborious fabrication process. Moreover, sample to sample variability makes experimentation with memristor-based synapses even harder. The usual alternatives are to either simulate or emulate the memristive systems under study. Both methodologies require the use of accurate modeling equations. In this paper, we present a diffusive compact model of memristive behavior that has already been experimentally validated. Furthermore, we implement an emulation architecture that enables us to freely explore the synapse-like characteristics of memristors. The main advantage of emulation over simulation is that the former allows us to work with real-world circuits. Our results can give some insight into the desirable characteristics of the memristors for neuromorphic applications.