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

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

This paper studies the Binary Neural Networks (BNNs) in which weights and activations are
both binarized into 1-bit values, thus greatly reducing the memory usage and computational …

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 …

This article focuses on filter-level network pruning. A novel pruning method, termed CLR-
RNF, is proposed. We first reveal a “long-tail” pruning problem in magnitude-based weight …

Convolutional network compression methods require training data for achieving acceptable
results, but training data is routinely unavailable due to some privacy and transmission …

Binarization of neural networks is a dominant paradigm in neural networks compression.
The pioneering work BinaryConnect uses Straight Through Estimator (STE) to mimic the …