In recent years, convolutional neural network (CNN) based methods have achieved great
success in a large number of applications and have been among the most powerful and widely …

DNNs (Deep Neural Networks) have demonstrated great success in numerous applications
such as image classification, speech recognition, video analysis, etc. However, DNNs are …

Machine learning and data mining are gaining increasing attentions of the computing society.
FPGA provides a highly parallel, low power, and flexible hardware platform for this domain…

Highly flexible software network functions (NFs) are crucial components to enable multi-tenancy
in the clouds. However, software packet processing on a commodity server has limited …

At the subcellular level, fat storage is confined to the evolutionarily conserved compartments
termed lipid droplets (LDs), which are closely associated with the endoplasmic reticulum (…

The recent emergence of Graphics Processing Units (GPUs) as general-purpose parallel
computing devices provides us with new opportunities to develop scalable learning methods …

Stereo vision is a well-known technique for acquiring depth information. In this paper, we
propose a real-time high-quality stereo vision system in field-programmable gate array (FPGA). …

The performance of large-scale graph processing suffers from challenges including poor
locality, lack of scalability, random access pattern, and heavy data conflicts. Some …

Dietary fat accumulates in lipid droplets or endolysosomal compartments that undergo selective
expansion under normal or pathophysiological conditions. We find that genetic defects …

The advancement of object detection algorithms makes them widely used in autonomous
systems. However, due to high computational complexity of Convolutional Neural Networks(…