Brain-inspired computing employs devices and architectures that emulate biological
functions for more adaptive and energy-efficient systems. Oscillatory neural networks …

Computing paradigm based on von Neuman architectures cannot keep up with the ever-
increasing data growth (also called “data deluge gap”). This has resulted in investigating …

Implementing scalable and effective synaptic networks will enable neuromorphic computing
to deliver on its promise of revolutionizing computing. RRAM represents the most promising …

This work proposes a digital implementation of an Oscillatory Neural Network (ONN) in a
Field-Programmable Gate Array (FPGA), demonstrating excellent associative memory …

Prolific growth of sensors and sensor technology has resulted various applications in
sensing, monitoring, assessment and control operations. Owing to the large number of …

One major challenge in efficiently implementing neuromorphic networks is the need for a
large number of variable synaptic connections. Networks that use emerging resistive …

Neuromorphic computing is a wide research field aimed to the realization of brain-inspired
hardware, apt to tackle computation of unstructured data more efficiently than currently done …

Digitalization of society creates important quantities of data that have been increasing at an
exponential rate during the past few years. Despite the tremendous technological progress …

In the last decades, the multiplication of edge devices in many industry domains drastically
increased the amount of data to treat and the complexity of tasks to solve, motivating the …

The concept of “information structures” in decentralized control is a formalization of the
notion of “who knows what and when do they know it.” Even seemingly simple problems with …