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 …

Due to the ability to implement customized topology, FPGA is increasingly used to deploy
SNNs in both embedded and high-performance applications. In this paper, we survey state …

Winograd is generally utilized to optimize convolution performance and computational
efficiency because of the reduced multiplication operations, but the reliability issues brought …

Machine learning is among the greatest advancements in computer science and
engineering and is today used to classify or detect objects, a key feature in autonomous …

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 …

Neuromorphic computing and spiking neural networks (SNNs) are gaining traction across
various artificial intelligence (AI) tasks thanks to their potential for efficient energy usage and …

Artificial Intelligence is an indispensable element of the modern world, constantly evolving
and contributing to the emergence of new technologies. We meet it in everyday applications …

We propose an on-line testing methodology for neuromorphic hardware supporting spiking
neural networks. Testing aims at detecting in real-time abnormal operation due to hardware …