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

Various hypotheses of information representation in brain, referred to as neural codes, have
been proposed to explain the information transmission between neurons. Neural coding …

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 …

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

The error-resiliency of Artificial Intelligence (AI) hardware accelerators is a major concern,
especially when they are deployed in mission-critical and safety-critical applications. In this …

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 …

As artificial neural networks have become increasingly integrated into safety-critical systems
such as autonomous vehicles, devices for medical diagnosis, and industrial automation …

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 …