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 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 …

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 …

Robotic technologies have been an indispensable part for improving human productivity
since they have been hel** humans in completing diverse, complex, and intensive tasks …

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

Recent trends have shown that autonomous agents, such as Autonomous Ground Vehicles
(AGVs), Unmanned Aerial Vehicles (UAVs), and mobile robots, effectively improve human …

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

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 …

We address the problem of testing artificial intelligence (AI) hardware accelerators
implementing spiking neural networks (SNNs). We define a metric to quickly rank available …