The enormous size of modern deep neural net-works makes it challenging to deploy those
models in memory and communication limited scenarios. Thus, compressing a trained …

Model compression is generally performed by using quantization, low-rank approximation or
pruning, for which various algorithms have been researched in recent years. One …

We propose a software framework based on the ideas of the Learning-Compression (LC)
algorithm, that allows a user to compress a neural network or other machine learning model …

After training complex deep learning models, a common task is to compress the model to
reduce compute and storage demands. When compressing, it is desirable to preserve the …

Deep neural networks frequently contain far more weights, represented at a higher
precision, than are required for the specific task which they are trained to perform …

The continued increase in memory, runtime and energy consumption of deployed machine
learning models on one side, and the trend to miniaturize intelligent devices and sensors on …

The recent focus on the efficiency of deep neural networks (DNNs) has led to significant
work on model compression approaches, of which weight pruning is one of the most …

Compression techniques for deep neural network models are becoming very important for
the efficient execution of high-performance deep learning systems on edge-computing …

Abstract Model compression is generally performed by using quantization, low-rank
approximation or pruning, for which various algorithms have been researched in recent …

With the rise in edge-computing devices, there has been an increasing demand to deploy
energy and resource-efficient models. A large body of research has been devoted to …