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 …

The security and privacy concerns along with the amount of data that is required to be
processed on regular basis has pushed processing to the edge of the computing systems …

The brain possesses the remarkable ability to seamlessly integrate perception with decision
making within a dynamically changing environment in a fault-tolerant, end-to-end manner …

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 …

Autonomous mobile agents (eg, mobile ground robots and UAVs) typically require low-
power/energy-efficient machine learning (ML) algorithms to complete their ML-based tasks …

Recent advances have shown that Spiking Neural Network (SNN)-based systems can
efficiently perform unsuper-vised continual learning due to their bio-plausible learning rule …

Spiking Neural Networks (SNNs) have shown capabilities of achieving high accuracy under
unsupervised settings and low operational power/energy due to their bio-plausible …

Research on Deep Neural Networks (DNNs) has focused on improving performance and
accuracy for real-world deployments, leading to new models, such as Spiking Neural …