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

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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Détails bibliographiques
Auteurs principaux: Stefano Bianchi, Irene Munoz-Martin, Erika Covi, Alessandro Bricalli, Giuseppe Piccoloni, Amir Regev, Gabriel Molas, Jean Francois Nodin, Francois Andrieu, Daniele Ielmini
Format: article
Langue:EN
Publié: IEEE 2021
Sujets:
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
Accès en ligne:https://doaj.org/article/37b5a4a1ad6f48178c41617d2bac5f0f
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