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 …

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 …

Spiking Neural Networks (SNNs) offer a promising solution to achieve ultra low-
power/energy computation for solving machine learning tasks. Currently, most of the SNN …

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

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

Spiking neural networks have made breakthroughs in computer vision by lending
themselves to neuromorphic hardware. However, the neuromorphic hardware lacks …

To maximize the performance and energy efficiency of Spiking Neural Network (SNN)
processing on resource-constrained embedded systems, specialized hardware …