Machine learning framework for the 1-transistor 1-memristor crossbar array. Demonstrations include convolutional neural network and convolutional long short-term memory network. For the manuscript "In situ training of feedforward and recurrent convolutional memristor networks" submitted to Nature Machine Intelligence
Neuromorphic computing describes the use of electrical circuits to mimic biological architecture pre...
Memristors offer great advantages as a new hardware solution for neuromorphic computing due to their...
Memristor crossbar is prevailing as one of the most promising candidates to construct the neural net...
MATLAB data files for the manuscript "In situ training of feedforward and recurrent convolutional me...
Memristive devices have shown great promise to facilitate the acceleration and improve the power eff...
Memristor is being considered as a game changer for the realization of neuromorphic hardware systems...
Abstract-This paper describes techniques to implement gradient-descent-based machine learning algori...
Power density constraint and device reliability issues are driving energy efficient, fault tolerant ...
Artificial neural networks (ANNs), such as the convolutional neural network (CNN) and long short-ter...
Memristive devices have shown great promise to facilitate the acceleration and improve the power eff...
Among the recent disruptive technologies, volatile/nonvolatile memory-resistor (memristor) has attra...
Memristive devices arranged in cross-bar architectures have shown great promise to facilitate the ac...
Neuromorphic systems are gaining signi cant importance in an era where CMOS digital techniques are r...
CMOS/Memristor integrated architectures have shown to be powerful for realizing energy-efficient lea...
The invention of neuromorphic computing architecture is inspired by the working mechanism of human-b...
Neuromorphic computing describes the use of electrical circuits to mimic biological architecture pre...
Memristors offer great advantages as a new hardware solution for neuromorphic computing due to their...
Memristor crossbar is prevailing as one of the most promising candidates to construct the neural net...
MATLAB data files for the manuscript "In situ training of feedforward and recurrent convolutional me...
Memristive devices have shown great promise to facilitate the acceleration and improve the power eff...
Memristor is being considered as a game changer for the realization of neuromorphic hardware systems...
Abstract-This paper describes techniques to implement gradient-descent-based machine learning algori...
Power density constraint and device reliability issues are driving energy efficient, fault tolerant ...
Artificial neural networks (ANNs), such as the convolutional neural network (CNN) and long short-ter...
Memristive devices have shown great promise to facilitate the acceleration and improve the power eff...
Among the recent disruptive technologies, volatile/nonvolatile memory-resistor (memristor) has attra...
Memristive devices arranged in cross-bar architectures have shown great promise to facilitate the ac...
Neuromorphic systems are gaining signi cant importance in an era where CMOS digital techniques are r...
CMOS/Memristor integrated architectures have shown to be powerful for realizing energy-efficient lea...
The invention of neuromorphic computing architecture is inspired by the working mechanism of human-b...
Neuromorphic computing describes the use of electrical circuits to mimic biological architecture pre...
Memristors offer great advantages as a new hardware solution for neuromorphic computing due to their...
Memristor crossbar is prevailing as one of the most promising candidates to construct the neural net...
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