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...

Full description

Bibliographic Details
Main Authors: Stefano Bianchi, Irene Munoz-Martin, Erika Covi, Alessandro Bricalli, Giuseppe Piccoloni, Amir Regev, Gabriel Molas, Jean Francois Nodin, Francois Andrieu, Daniele Ielmini
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Subjects:
Catastrophic forgetting
complementary learning systems
continual learning
convolutional neural networks (CNNs)
resistive switching random-access memory (RRAM)
spike-timing-dependent plasticity (STDP)
Online Access:https://ieeexplore.ieee.org/document/9565857/

Internet

https://ieeexplore.ieee.org/document/9565857/

Similar Items