Deep learning (DL) has recently changed the development of intelligent systems and is
widely adopted in many real-life applications. Despite their various benefits and potentials …

This paper presents an extensive literature review on Binary Neural Network (BNN). BNN
utilizes binary weights and activation function parameters to substitute the full-precision …

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

Learning to synthesize data has emerged as a promising direction in zero-shot quantization
(ZSQ), which represents neural networks by low-bit integer without accessing any of the real …

Network binarization emerges as one of the most promising compression approaches
offering extraordinary computation and memory savings by minimizing the bit-width …

Binary neural networks (BNNs) have received increasing attention due to their superior
reductions of computation and memory. Most existing works focus on either lessening the …

Abstract Model binarization can significantly compress model size, reduce energy
consumption, and accelerate inference through efficient bit-wise operations. Although …

Recently, there have been tremendous efforts in develo** lightweight Deep Neural
Networks (DNNs) with satisfactory accuracy, which can enable the ubiquitous deployment of …

Artificial intelligence technologies based on deep convolutional neural networks and large
language models have made significant breakthroughs in many tasks, such as image …

Recent advancements in machine learning achieved by Deep Neural Networks (DNNs)
have been significant. While demonstrating high accuracy, DNNs are associated with a …