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

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 …

The adoption of deep neural networks (DNNs) in safety-critical domains has engendered
serious reliability concerns. A prominent example is hardware transient faults that are …

Deep neural network (DNN) training workloads are increasingly susceptible to hardware
failures in datacenters. For example, Google experienced" mysterious, difficult to identify …

Transformers have become keystone models in natural language processing over the past
decade. They have achieved great popularity in deep learning applications, but the …

The security and privacy concerns along with the amount of data that is required to be
processed on regular basis has pushed processing to the edge of the computing systems …

DistServe improves the performance of large language models (LLMs) serving by
disaggregating the prefill and decoding computation. Existing LLM serving systems colocate …

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

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

In this article, we approach to construct reliable deep neural networks (DNNs) for safety-
critical artificial intelligent applications. We propose to modify rectified linear unit (ReLU), a …