Designing energy efficient and scalable artificial networks for neuromorphic computing remains a challenge. Here, the authors demonstrate online learning in a monolithically integrated 4 × 4 fully memristive neural network consisting of volatile NbOx memristor neurons and nonvolatile TaOx memristor synapses
We present new computational building blocks based on memristive devices. These blocks, can be used ...
Memristive devices have emerged as compact nonvolatile memory elements which can be used as synapses...
Abstract—We present new computational building blocks based on memristive devices. These blocks, can...
Neuromorphic computing has shown great advantages towards cognitive tasks with high speed and remark...
Biologically plausible neuromorphic computing systems are attracting considerable attention due to t...
The proliferation of machine learning algorithms in everyday applications such as image recognition ...
In the new era of cognitive computing, systems will be able to learn and interact with the environme...
Spike-based learning with memristive devices in neuromorphic computing architectures typically uses ...
© 2019 by the authors.Inspired by biology, neuromorphic systems have been trying to emulate the huma...
On metrics of density and power efficiency, neuromorphic technologies have the potential to surpass ...
Neuromorphic engineering is the research field dedicated to the study and design of brain-inspired h...
Recent breakthroughs in neuromorphic computing show that local forms of gradient descent learning ar...
Artificial Intelligence has found many applications in the last decade due to increased computing po...
This dissertation is dedicated to using Memristive Spiking Neural Networks (MSNNs) for deep learning...
Memristive devices present a new device technology allowing for the realization of compact non-volat...
We present new computational building blocks based on memristive devices. These blocks, can be used ...
Memristive devices have emerged as compact nonvolatile memory elements which can be used as synapses...
Abstract—We present new computational building blocks based on memristive devices. These blocks, can...
Neuromorphic computing has shown great advantages towards cognitive tasks with high speed and remark...
Biologically plausible neuromorphic computing systems are attracting considerable attention due to t...
The proliferation of machine learning algorithms in everyday applications such as image recognition ...
In the new era of cognitive computing, systems will be able to learn and interact with the environme...
Spike-based learning with memristive devices in neuromorphic computing architectures typically uses ...
© 2019 by the authors.Inspired by biology, neuromorphic systems have been trying to emulate the huma...
On metrics of density and power efficiency, neuromorphic technologies have the potential to surpass ...
Neuromorphic engineering is the research field dedicated to the study and design of brain-inspired h...
Recent breakthroughs in neuromorphic computing show that local forms of gradient descent learning ar...
Artificial Intelligence has found many applications in the last decade due to increased computing po...
This dissertation is dedicated to using Memristive Spiking Neural Networks (MSNNs) for deep learning...
Memristive devices present a new device technology allowing for the realization of compact non-volat...
We present new computational building blocks based on memristive devices. These blocks, can be used ...
Memristive devices have emerged as compact nonvolatile memory elements which can be used as synapses...
Abstract—We present new computational building blocks based on memristive devices. These blocks, can...