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

Convolutional neural network (CNN) pruning has become one of the most successful
network compression approaches in recent years. Existing works on network pruning …

We propose ResRep, a novel method for lossless channel pruning (aka filter pruning), which
slims down a CNN by reducing the width (number of output channels) of convolutional …

Advanced tensor decomposition, such as Tensor train (TT) and Tensor ring (TR), has been
widely studied for deep neural network (DNN) model compression, especially for recurrent …

We present a novel global compression framework for deep neural networks that
automatically analyzes each layer to identify the optimal per-layer compression ratio, while …

Deep learning technology has found a promising application in lightweight model design, for
which pruning is an effective means of achieving a large reduction in both model parameters …

Abstract Visual Pattern Recognition Networks (VPRNs) are widely used in various visual
data based applications such as computer vision and edge AI. VPRNs help to enhance a …

Despite achieving remarkable efficiency, traditional network pruning techniques often follow
manually-crafted heuristics to generate pruned sparse networks. Such heuristic pruning …

Deep neural networks have shown impressive performance in many areas, including
computer vision and natural language processing. Millions of parameters in deep neural …

Applying a pre-trained large model to downstream tasks is prohibitive under resource-
constrained conditions. Recent dominant approaches for addressing efficiency issues …