Abstract Knowledge extraction through machine learning techniques has been successfully
applied in a large number of application domains. However, apart from the required …

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 …

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

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

The advent of High-Performance Computing has led to the adoption of Convolutional Neural
Networks (CNNs) in safety-critical applications such as autonomous vehicles. However …

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

With the rapid progress of deep neural network (DNN) applications on memristive platforms,
there has been a growing interest in the acceleration and compression of memristive …

Winograd convolution is originally proposed to reduce the computing overhead by
converting multiplication in neural network (NN) with addition via linear transformation. Other …

With the rapid advancements of deep learning in the past decade, it can be foreseen that
deep learning will be continuously deployed in more and more safety-critical applications …

Soft errors in large VLSI circuits have a significant impact on computing-and memory-
intensive neural network (NN) processing. Understanding the influence of soft errors on NNs …