Logic Computing with Stateful Neural Networks of Resistive Switches

Stateful STT-MRAM- Based Logic for Beyond–Von Neumann Computing

Computing First-Order Logic Programs by Fibring Artificial Neural Networks

The integration of symbolic and neural-network-based artificial intelligence paradigms constitutes a very challenging area of research. The overall aim is to merge these two very different major approaches to intelligent systems engineering while retaining their respective strengths. For symbolic paradigms that use the syntax of some first-order language this appears to be particularly difficul...

Computing with Truly Asynchronous Threshold Logic Networks

We present simulation mechanisms by which any network of threshold logic units with either symmetric or asymmetric interunit connections (i.e., a symmetric or asymmetric \Hoppeld net") can be simulated on a network of the same type, but without any a priori constraints on the order of updates of the units. Together with earlier constructions, the results show that the truly asynchronous network...

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices

In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN) in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU) devices to convolutional neural networks (CNNs). We show how to map the convolutional layers to fully connected RPU arrays ...

Data retention in organic ferroelectric resistive switches

Solution-processed organic ferroelectric resistive switches could become the long-missing non-volatile memory elements in organic electronic devices. To this end, data retention in these devices should be characterized, understood and controlled. First, it is shown that the measurement protocol can strongly affect the ‘apparent’ retention time and a suitable protocol is identified. Second, it i...