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

Artificial Intelligence (AI) and, in particular, Machine Learning (ML), have emerged to be
utilized in various applications due to their capability to learn how to solve complex …

High accuracy and ever-increasing computing power have made deep neural networks
(DNNs) the algorithm of choice for various machine learning, computer vision, and image …

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 …

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 …

Prior works typically conducted the fault analysis of neural network accelerator computing
arrays with simulation and focused on the prediction accuracy loss of the neural network …

High-performance embedded systems with powerful processors, specialized hardware
accelerators, and advanced software techniques are all key technologies driving the growth …

Hardware faults on the regular 2-D computing array of a typical deep learning accelerator
(DLA) can lead to dramatic prediction accuracy loss. Prior redundancy design approaches …

Convolutional neural networks (CNNs) are widely used in computer vision and natural
language processing. Field-programmable gate arrays (FPGAs) are popular accelerators for …

As convolutional neural network CNN accelerators are being adopted in emerging safety-
critical areas, their reliability becomes prominent. The systolic array is widely used as the …