Beyond energy, the growing number of defects in physical substrates is becoming another
major constraint that affects the design of computing devices and systems. As the underlying …

As DNNs become increasingly common in mission-critical applications, ensuring their
reliable operation has become crucial. Conventional resilience techniques fail to account for …

This study is concerned with the adaptive neural network (NN) observer design problem for
continuous-time switched systems via quantized output signals. A novel NN observer is …

Deep learning tasks cover a broad range of domains and an even more extensive range of
applications, from entertainment to extremely safety-critical fields. Thus, Deep Neural …

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

Deep neural networks (DNNs) have become one of the enabling technologies in many
safety-critical applications, eg, autonomous driving and medical image analysis. DNN …

In this paper, a new robust fault-tolerant compensation control method for uncertain linear
systems over networks is proposed, where only quantized signals are assumed to be …

This article investigates the issue of speed tracking and dynamic adjustment of headway for
the repeatable multiple subway trains (MSTs) system in the case of actuator faults. First, the …

Applying deep neural networks (DNNs) in mobile and safety-critical systems, such as
autonomous vehicles, demands a reliable and efficient execution on hardware. The design …

In the modern era, artificial intelligence (AI) and deep learning (DL) seamlessly integrate into
various spheres of our daily lives. These cutting-edge disciplines have given rise to …