Supervised Learning in All FeFET-Based Spiking Neural Network: Opportunities and Challenges.

Dutta, Sourav; Schafer, Clemens; Gomez, Jorge; Ni, Kai; Joshi, Siddharth; Datta, Suman

Dutta, Sourav; Schafer, Clemens; Gomez, Jorge; Ni, Kai; Joshi, Siddharth; Datta, Suman.

Afiliação

Dutta S; Department of Electrical Engineering, College of Engineering, University of Notre Dame, Notre Dame, IN, United States.
Schafer C; Department of Computer Science and Engineering, College of Engineering, University of Notre Dame, Notre Dame, IN, United States.
Gomez J; Department of Electrical Engineering, College of Engineering, University of Notre Dame, Notre Dame, IN, United States.
Ni K; Department of Microsystems Engineering, Rochester Institute of Technology, Rochester, NY, United States.
Joshi S; Department of Computer Science and Engineering, College of Engineering, University of Notre Dame, Notre Dame, IN, United States.
Datta S; Department of Electrical Engineering, College of Engineering, University of Notre Dame, Notre Dame, IN, United States.

Front Neurosci ; 14: 634, 2020.

Article em En | MEDLINE | ID: mdl-32670012

Palavras-chave

analog synapse; ferroelectric FET; neuromorphic computing; spiking neural network; spiking neuron; supervised learning; surrogate gradient learning

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Front Neurosci Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google