The remarkable performance of deep Convolutional neural networks (CNNs) is generally
attributed to their deeper and wider architectures, which can come with significant …

Deep Neural Networks (DNNs) have gained unprecedented popularity due to their high-
order performance and automated feature extraction capability. This has encouraged …

Abstract Recently, Vision Transformer (ViT) has continuously established new milestones in
the computer vision field, while the high computation and memory cost makes its …

Recently, a new trend of exploring sparsity for accelerating neural network training has
emerged, embracing the paradigm of training on the edge. This paper proposes a novel …

Channel pruning has been broadly recognized as an effective technique to reduce the
computation and memory cost of deep convolutional neural networks. However …

Recent work demonstrated the promise of using resistive random access memory (ReRAM)
as an emerging technology to perform inherently parallel analog domain in-situ matrix …

The ever-increasing number of Internet of Things (IoT) devices are continuously generating
huge masses of data, but the current cloud-centric approach for IoT big data analysis has …

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

This paper critically examines model compression techniques within the machine learning
(ML) domain, emphasizing their role in enhancing model efficiency for deployment in …

Abstract Generative Adversarial Networks (GANs) have achieved huge success in
generating high-fidelity images, however, they suffer from low efficiency due to tremendous …