In this work, we review Binarized Neural Networks (BNNs). BNNs are deep neural networks
that use binary values for activations and weights, instead of full precision values. With …

With the recent success of the deep neural networks (DNNs) in the field of artificial
intelligence, the urge of deploying DNNs has drawn tremendous attention because it can …

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

Deep neural networks have been applied in many applications exhibiting extraordinary
abilities in the field of computer vision. However, complex network architectures challenge …

The binary neural network, largely saving the storage and computation, serves as a
promising technique for deploying deep models on resource-limited devices. However, the …

Abstract Knowledge distillation is a widely applicable technique for training a student neural
network under the guidance of a trained teacher network. For example, in neural network …

Pretrained large language models (LLMs) exhibit exceptional general language processing
capabilities but come with significant demands on memory and computational resources. As …

In this paper, we propose several ideas for enhancing a binary network to close its accuracy
gap from real-valued networks without incurring any additional computational cost. We first …

Recent machine learning methods use increasingly large deep neural networks to achieve
state of the art results in various tasks. The gains in performance come at the cost of a …

Reducing bit-widths of activations and weights of deep networks makes it efficient to
compute and store them in memory, which is crucial in their deployments to resource-limited …