Abstract Deep Neural Network (DNN) models for both resource-rich environments and
resource-constrained devices have become abundant in recent years. As of now, the …

Sparse-sparse matrix multiplication (SpGEMM) is a computation kernel widely used in
numerous application domains such as data analytics, graph processing, and scientific …

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

The unprecedented progress in computational technologies led to a substantial proliferation
of artificial intelligence applications, notably in the era of big data and IoT devices. In the …

Tensor factorizations are powerful tools in many machine learning and data analytics
applications. Tensors are often sparse, which makes sparse tensor factorizations memory …

Machine learning (ML) models are widely used in many important domains. For efficiently
processing these computational-and memory-intensive applications, tensors of these …

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 …

Electrocardiography (ECG) has been used as a diagnostic tool for various heart diseases. It
is most effective in detecting myocardial infarction and fatal arrhythmias. This work proposes …

Recognition tasks, such as object recognition and keypoint estimation, have seen
widespread adoption in recent years. Most state-of-the-art methods for these tasks use deep …

Due to complex interactions among various deep neural network (DNN) optimization
techniques, modern DNNs can have weights and activations that are dense or sparse with …