Abstract
Memristors are widely considered as promising elements for the efficient implementation of synaptic weights in artificial neural networks (ANNs) since they are resistors that keep memory of their previous conductive state. Whereas demonstrations of simple neural networks (e.g., a single-layer perceptron) based on memristors already exist, the implementation of more complicated networks is more challenging and has yet to be reported. In this study, we demonstrate linearly nonseparable combinational logic classification (XOR logic task) using a network implemented with CMOS-based neurons and organic memrisitive devices that constitutes the first step toward the realization of a double layer perceptron. We also show numerically the ability of such network to solve a principally analogue task which cannot be realized by digital devices. The obtained results prove the possibility to create a multilayer ANN based on memristive devices that paves the way for designing a more complex network such as the double layer perceptron.
Highlights
The development and hardware realization of artificial neural networks that are capable of learning information processing remains one of the most challenging tasks in artificial intelligence
The main goal of the present work is the hardware realization of a simple double-layer artificial neural networks (ANNs) based on organic memristive devices able to solve linearly nonseparable tasks. In this manuscript we present the first steps towards the realization of the double layer perceptron, including the design and hardware realization of the ANN
Vital requirement for training the network is the ability to change the resistance of every memristive device independently from others. To manage this issue we developed an access system based on CMOS-transistors as the voltage-controlled switches
Summary
The development and hardware realization of artificial neural networks that are capable of learning information processing (pattern recognition and classification, approximation, prediction, etc.) remains one of the most challenging tasks in artificial intelligence. There are two main possible ways of synapse realization: a digital one (e.g., as the Static Random Access Memory[2] or floating gate transistor3) and an analogue one (memristive device).[4] The main advantage of the first one is its full integration with the standard CMOS technology This approach suffers from i) digital versus analogue representation of synaptic weights reflecting the lower performance of ANNs’ super-parallel computations; ii) mediocre energy efficiency, if compared to memristive systems and to their biological counterparts, iii) the chip has a lower potential density than in case of memristors use. A memristive device is a two-terminal device, whose conductivity may be changed almost continuously by applying a relatively large voltage bias, but is retained constant when a smaller bias or no bias is applied.[6]
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call