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 …

Convolutional Neural Networks (CNNs) are currently one of the most widely used predictive
models in machine learning. Recent studies have demonstrated that hardware faults …

Modern neural network complexity has grown dramatically in recent years, leading to the
adoption of hardware-accelerated solutions to cope with the computational power required …

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

Deep Neural Networks (DNNs) and their accelerators are being deployed ever more
frequently in safety-critical applications leading to increasing reliability concerns. A …

Vision transformers (ViTs) that leverage self-attention mechanism have shown superior
performance on many classical vision tasks compared to convolutional neural networks …

This article presents a platform and design appr-oach for enabling radiation-tolerant deep
learning acceleration on static random access memory (SRAM)-based 20-nm Kintex …

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 …

The rapid advancement of Artificial Intelligence (AI) has led to its integration into various
areas, especially with Large Language Models (LLMs) significantly enhancing capabilities …