Abstract Neural Architecture Search (NAS) aims to automatically discover optimal
architectures. In this paper, we propose an hourglass-inspired approach (HourNAS) for …

As convolution layers have been proved to be the most time-consuming operation in
convolutional neural network (CNN) algorithms, many efficient CNN accelerators have been …

Recent progress in deep learning techniques enabled collaborative edge training, which
usually deploys identical neural network models globally on multiple devices for …

Nowadays, the evolution of deep learning and cloud service significantly promotes neural
network-based mobile applications. Although intelligent and prolific, those applications still …

This paper gives an overview of our ongoing work on the design space exploration of
efficient deep neural networks (DNNs). Specifically, we cover two aspects:(1) static …

Abstract For many popular Convolutional Neural Networks (CNNs), memory has become
one of the major constraints for their efficient training and inference on edge devices …