Alternative negative weight for simpler hardware implementation of synapse device based neuromorphic system
Abstract Lately, there has been a rapid increase in the use of software-based deep learning neural networks (S-DNN) for the analysis of unstructured data consumption. For implementation of the S-DNN, synapse-device-based hardware DNN (H-DNN) has been proposed as an alternative to typical Von-Neumann...
Enregistré dans:
Auteurs principaux: | Geonhui Han, Chuljun Lee, Jae-Eun Lee, Jongseon Seo, Myungjun Kim, Yubin Song, Young-Ho Seo, Daeseok Lee |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/aec75e692f634a0a90acfdf00a3e5311 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Neuromorphic computing with multi-memristive synapses
par: Irem Boybat, et autres
Publié: (2018) -
Multi-Terminal Memristive Devices Enabling Tunable Synaptic Plasticity in Neuromorphic Hardware: A Mini-Review
par: Yann Beilliard, et autres
Publié: (2021) -
Challenges hindering memristive neuromorphic hardware from going mainstream
par: Gina C. Adam, et autres
Publié: (2018) -
A robust model of Stimulus-Specific Adaptation validated on neuromorphic hardware
par: Natacha Vanattou-Saïfoudine, et autres
Publié: (2021) -
Graphene memristive synapses for high precision neuromorphic computing
par: Thomas F. Schranghamer, et autres
Publié: (2020)