Neuromorphic computing based on Spiking Neural Networks (SNNs) is an emerging
computing paradigm inspired by the functionality of the biological brain. Given its potential to …

Machine learning is among the greatest advancements in computer science and
engineering and is today used to classify or detect objects, a key feature in autonomous …

Neuromorphic computing, a brain inspired non-Von Neumann computing system, addresses
the challenges posed by the Moore's law memory wall phenomenon. It has the capability to …

Neuromorphic computing and spiking neural networks (SNNs) are gaining traction across
various artificial intelligence (AI) tasks thanks to their potential for efficient energy usage and …

Resistive random access Memory (RRAM) based spiking neural networks (SNN) are
becoming increasingly attractive for pervasive energy-efficient classification tasks. However …

The increase in complexity of Machine Learning (ML) algorithms, specifically Deep Neural
Networks (DNN), and the high demand to deploy them on resource-constrained edge …

We present a novel test generation algorithm for hardware accelerators of Spiking Neural
Networks (SNNs). The algorithm is based on advanced optimization tailored for the spiking …

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 recent rise of Artificial Intelligence (AI) has found a wide range of applications
essentially integrating it gaining more and more ground in almost every field of our lives …