Combining Accuracy and Plasticity in Convolutional Neural Networks Based on Resistive Memory Arrays for Autonomous Learning

Nowadays, artificial neural networks (ANNs) can outperform the human brain’s ability in specific tasks. However, ANNs cannot replicate the efficient and low-power learning, adaptation, and consolidation typical of biological organisms. Here, we present a hardware design based on arrays of...

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Autores principales:	Stefano Bianchi, Irene Munoz-Martin, Erika Covi, Alessandro Bricalli, Giuseppe Piccoloni, Amir Regev, Gabriel Molas, Jean Francois Nodin, Francois Andrieu, Daniele Ielmini
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Catastrophic forgetting complementary learning systems continual learning convolutional neural networks (CNNs) resistive switching random-access memory (RRAM) spike-timing-dependent plasticity (STDP) Computer engineering. Computer hardware TK7885-7895
Acceso en línea:	https://doaj.org/article/37b5a4a1ad6f48178c41617d2bac5f0f
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Combining Accuracy and Plasticity in Convolutional Neural Networks Based on Resistive Memory Arrays for Autonomous Learning

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