We review the problem of automating hardware-aware architectural design process of Deep
Neural Networks (DNNs). The field of Convolutional Neural Network (CNN) algorithm design …

Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning
(DL) is already present in many applications ranging from computer vision for medicine to …

Neural network models, celebrated for their outstanding scalability and computational
capabilities, have demonstrated remarkable performance across various fields such as …

Energy efficiency is critical for running computer vision on battery-powered systems, such as
mobile phones or UAVs (unmanned aerial vehicles, or drones). This book collects the …

Measuring Efficiency in neural network system development is an open research problem.
This paper presents an experimental framework to measure the training efficiency of a …

In this work, we present a differentiable neural architecture search (NAS) method that takes
into account two competing objectives, quality of result (QoR) and quality of service (QoS) …

Designing suitable neural networks in resource-constrained scenarios is a very challenging
problem. It means the network architecture must not only have excellent performance on the …

Neural Architecture Search (NAS) has received extensive attention due to its capability to
discover neural network architectures in an automated manner. aw_nas is an open-source …

Recent advancement in algorithm is pushing forward the application of autonomous driving
in real life. The massive usage of deep learning in autonomous driving is making the system …

Autonomous driving is becoming a hot topic in both academic and industrial communities.
Traditional algorithms can hardly achieve the complex tasks and meet the high safety …