Ponencia
Implementation of binary stochastic STDP learning using chalcogenide-based memristive devices
Autor/es | Mohan, Charanraj
Camuñas Mesa, Luis Alejandro Rosa Utrera, José Manuel de la Serrano Gotarredona, María Teresa Linares Barranco, Bernabé |
Departamento | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores |
Fecha de publicación | 2021-04 |
Fecha de depósito | 2021-06-28 |
Publicado en |
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ISBN/ISSN | 978-1-7281-9201-7 978-1-7281-9202-4 |
Resumen | The emergence of nano-scale memristive devices
encouraged many different research areas to exploit their use
in multiple applications. One of the proposed applications was
to implement synaptic connections in bio-inspired ... The emergence of nano-scale memristive devices encouraged many different research areas to exploit their use in multiple applications. One of the proposed applications was to implement synaptic connections in bio-inspired neuromorphic systems. Large-scale neuromorphic hardware platforms are being developed with increasing number of neurons and synapses, having a critical bottleneck in the online learning capabilities. Spiketiming- dependent plasticity (STDP) is a widely used learning mechanism inspired by biology which updates the synaptic weight as a function of the temporal correlation between pre- and postsynaptic spikes. In this work, we demonstrate experimentally that binary stochastic STDP learning can be obtained from a memristor when the appropriate pulses are applied at both sides of the device. |
Identificador del proyecto | PID2019-105556GB-C31
PID2019-103876RB-I00 (CORDION) TEC2015- 63884-C2-1-P (COGNET) US-1260118 (Neuro-Radio) |
Cita | Mohan, C., Camuñas Mesa, L.A., Rosa Utrera, J.M.d.l., Serrano Gotarredona, M.T. y Linares Barranco, B. (2021). Implementation of binary stochastic STDP learning using chalcogenide-based memristive devices. En 2021 IEEE International Symposium on Circuits and Systems (ISCAS) Daegu, Korea (South): IEEE. |
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