A convolutional neural network (CNN) is one of the most significant networks in the deep
learning field. Since CNN made impressive achievements in many areas, including but not …

In this community review report, we discuss applications and techniques for fast machine
learning (ML) in science—the concept of integrating powerful ML methods into the real-time …

This chapter provides approaches to the problem of quantizing the numerical values in deep
Neural Network computations, covering the advantages/disadvantages of current methods …

Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …

Deep learning-based face recognition models follow the common trend in deep neural
networks by utilizing full-precision floating-point networks with high computational costs …

Autonomous driving services depends on active sensing from modules such as camera,
LiDAR, radar, and communication units. Traditionally, these modules process the sensed …

Pruning is an effective method to reduce the memory footprint and FLOPs associated with
neural network models. However, existing structured pruning methods often result in …

This paper introduces deformable convolution in deep learning based joint denoising and
demosaicing (JDD), which yields more adaptable representation and larger receptive fields …

Automatic algorithm-hardware co-design for DNN has shown great success in improving the
performance of DNNs on FPGAs. However, this process remains challenging due to the …

The development of object detection has led to huge improvements in human interaction
systems. Object detection is a challenging task because it involves many parameters …