Currently, with the rapid development of deep learning, deep neural networks (DNNs) have
been widely applied in various computer vision tasks. However, in the pursuit of …

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 …

Light-weight convolutional neural networks (CNNs) are specially designed for applications
on mobile devices with faster inference speed. The convolutional operation can only capture …

In recent years, machine learning (ML) and deep learning (DL) have shown remarkable
improvement in computer vision, natural language processing, stock prediction, forecasting …

Despite the fact that deep neural networks are powerful models and achieve appealing
results on many tasks, they are too large to be deployed on edge devices like smartphones …

Previous works utilized" smaller-norm-less-important" criterion to prune filters with smaller
norm values in a convolutional neural network. In this paper, we analyze this norm-based …

Recent advancements in machine learning achieved by Deep Neural Networks (DNNs)
have been significant. While demonstrating high accuracy, DNNs are associated with a …

This paper proposed a Soft Filter Pruning (SFP) method to accelerate the inference
procedure of deep Convolutional Neural Networks (CNNs). Specifically, the proposed SFP …

State-of-the-art CNN based recognition models are often computationally prohibitive to
deploy on low-end devices. A promising high level approach tackling this limitation is …

In recent years, deep neural networks (DNNs) have received increased attention, have been
applied to different applications, and achieved dramatic accuracy improvements in many …