Hardware realization of artificial intelligence (AI) requires new design styles and even
underlying technologies than those used in traditional digital processors or logic circuits …

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 …

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 …

We propose an on-line testing methodology for neuromorphic hardware supporting spiking
neural networks. Testing aims at detecting in real-time abnormal operation due to hardware …

The last few decades have witnessed a dramatic evolution of Artificial Intelligence (AI)
algorithms, represented by Deep Neural Networks (DNNs), resulting in AI-enabled systems …

Bayesian Neural Networks (BayNNs) can inherently estimate predictive uncertainty,
facilitating informed decision-making. Dropout-based BayNNs are increasingly implemented …

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

Neuromorphic chips are promising hardware implementations for artificial intelligence (AI)
applications owing to their low power consumption. However, neuromorphic chips are …

Time-to-first-spike (TTFS) encoded spiking neural networks (SNNs), implemented using
memristive crossbar arrays (MCA), achieve higher inference speed and energy efficiency …

In the last decade, neural networks (NNs) have established themselves as the foundation of
modern computer vision, achieving remarkable performances in a consistent number of …