Neural network pruning and quantization techniques are almost as old as neural networks
themselves. However, to date, only ad-hoc comparisons between the two have been …

Deep neural networks (DNNs) have achieved great success in many object detection tasks.
However, such DNNS-based large object detection models are generally computationally …

Abstract Visual Pattern Recognition Networks (VPRN) delivers high performance using deep
neural networks. With the advancements in deep neural networks VPR network has gained …

In recent years, many researchers focused on unsupervised learning for network anomaly
detection in edge devices to identify attacks. The deployment of the unsupervised …

Automatic modulation recognition (AMR) plays an important role in communication system.
With the expansion of data volume and the development of computing power, deep learning …

Abstract Quantization Neural Networks (QNN) have attracted a lot of attention due to their
high efficiency. To enhance the quantization accuracy, prior works mainly focus on …

Mixed-precision Deep Neural Networks (DNNs) provide an efficient solution for hardware
deployment, especially under resource constraints, while maintaining model accuracy …

Computing has undergone a significant transformation over the past two decades, shifting
from a machine-based approach to a human-centric, virtually invisible service known as …

The emergence of powerful deep learning architectures has resulted in breakthrough
innovations in several fields such as healthcare, precision farming, banking, education, and …

Recently, the sizes of deep neural networks and training datasets both increase drastically
to pursue better performance in a practical sense. With the prevalence of transformer-based …