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

The rapid progress of autonomous driving (AD) has triggered a surge in demand for safer
and more efficient autonomous vehicles, owing to the intricacy of modern urban …

Spiking neural networks (SNNs) have shown a potential for having low energy with
unsupervised learning capabilities due to their biologically-inspired computation. However …

Specialized hardware accelerators have been designed and employed to maximize the
performance efficiency of Spiking Neural Networks (SNNs). However, such accelerators are …

Despite the parallelism and sparsity in neural network models, their transfer into hardware
unavoidably makes them susceptible to hardware-level faults. Hardware-level faults can …

Spiking neural networks (SNNs) have demonstrated excellent capabilities in various
intelligent scenarios. Most existing methods for training SNNs are based on the concept of …

In the literature, it is argued that Deep Neural Networks (DNNs) possess a certain degree of
robustness mainly for two reasons: their distributed and parallel architecture, and their …

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 uses spiking neuron network models to solve machine learning
problems in a more energy-efficient way when compared to conventional artificial neural …

Deep neural network (DNN) models are being deployed in safety-critical embedded devices
for object identification, recognition, and even trajectory prediction. Optimized versions of …