Since Google proposed Transformer in 2017, it has made significant natural language
processing (NLP) development. However, the increasing cost is a large amount of …

Today, convolutional neural networks (CNNs) are widely used in computer vision
applications. However, the trends of higher accuracy and higher resolution generate larger …

In limited-resource edge computing circumstances such as on mobile devices, IoT devices,
and electric vehicles, the energy-efficient optimized convolutional neural network (CNN) …

Deep Neural Networks (DNNs) have achieved great progress in academia and industry. But
they have become computational and memory intensive with the increase of network depth …

In point cloud classification tasks, efficiently extracting point cloud data feature has always
been a challenging problem. Based on the characteristics of the point cloud data …

Convolutional neural networks (CNNs)(including 2D and 3D convolutions) are popular in
video analysis tasks such as action recognition and activity understanding. Fast algorithms …

Recently, algorithm and hardware co-design for neu-ral networks (NNs) has become the key
to obtaining high-quality solutions. However, prior works lack consideration of the underlying …

Abstract Mamba, a State Space Model (SSM), has recently shown competitive performance
to Convolutional Neural Networks (CNNs) and Transformers in Natural Language …

Attention-based transformers have achieved significant performance breakthroughs in
natural language processing (NLP) and computer vision (CV) tasks. Meanwhile, the ever …

Convolutional neural networks (CNNs) are widely utilized in intelligent edge computing
applications such as computational vision and image processing. However, as the number …