Domain-specific hardware is becoming a promising topic in the backdrop of improvement
slow down for general-purpose processors due to the foreseeable end of Moore's Law …

Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

Filter pruning has been widely used for neural network compression because of its enabled
practical acceleration. To date, most of the existing filter pruning works explore the …

The advent of Deep Learning (DL) has radically transformed the computing industry across
the entire spectrum from algorithms to circuits. As myriad application domains embrace DL, it …

Convolutional neural networks (CNNs) are emerging as powerful tools for image
processing. Recent machine learning work has reduced CNNs' compute and data volumes …

In recent years, attention-based models have achieved impressive performance in natural
language processing and computer vision applications by effectively capturing contextual …

Deep convolutional neural networks (CNN) have achieved remarkable performance with the
cost of huge computation. As the CNN model becomes more complex and deeper …

Deep convolutional neural networks (CNNs) have achieved state-of-the-art performance in a
wide range of applications. However, deeper CNN models, which are usually computation …

The co-existence of activation sparsity and model sparsity in convolutional neural network
(CNN) models makes sparsity-aware CNN hardware designs very attractive. The existing …

With the rise of artificial intelligence in recent years, Deep Neural Networks (DNNs) have
been widely used in many domains. To achieve high performance and energy efficiency …