The recurrent neural networks (RNN) found to be an effective tool for approximating dynamic
systems dealing with time and order dependent data such as video, audio and others. Long …

As the limits of transistor technology are approached, feature size in integrated circuit
transistors has been reduced very near to the minimum physically-realizable channel length …

The memristor is a resistive switch where its resistive state is programable based on the
applied voltage or current. Memristive devices are thus capable of storing and computing …

Recent breakthroughs in recurrent deep neural networks with long short-term memory
(LSTM) units have led to major advances in artificial intelligence. However, state-of-the-art …

The volume, veracity, variability, and velocity of data produced from the ever increasing
network of sensors connected to Internet pose challenges for power management …

Long short-term memory (LSTM) with significantly increased complexity and a large number
of parameters have a bottleneck in computing power resulting from limited memory capacity …

The rapid development of artificial intelligence (AI), big data analytics, cloud computing, and
Internet of Things applications expect the emerging memristor devices and their hardware …

The on-chip implementation of learning algorithms would speed up the training of neural
networks in crossbar arrays. The circuit level design and implementation of a back …

Neuromorphic computing is expected to realize fast and energy-efficient artificial neural
networks and address the inherent limitations of von Neumann architectures in dedicated …

Memristive LSTM networks have been proven as a powerful Neuromorphic Computing
Architecture (NCA) for various time series forecasting tasks and are recognized as the next …