The remarkable performance of deep Convolutional neural networks (CNNs) is generally
attributed to their deeper and wider architectures, which can come with significant …

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 …

Diffusion models have recently dominated image synthesis and other related generative
tasks. However, the iterative denoising process is expensive in computations at inference …

Large Language Models (LLMs) excel in NLP, but their demands hinder their widespread
deployment. While Quantization-Aware Training (QAT) offers a solution, its extensive …

Most existing neural network pruning methods hand-crafted their importance criteria and
structures to prune. This constructs heavy and unintended dependencies on heuristics and …

Large foundation models, including large language models (LLMs), vision transformers
(ViTs), diffusion, and LLM-based multimodal models, are revolutionizing the entire machine …

Sparsity is becoming arguably the most critical dimension to explore for efficiency and
scalability, as deep learning models grow significantly larger and more complex. After all …

Pruning is an effective method to reduce the memory footprint and FLOPs associated with
neural network models. However, existing structured pruning methods often result in …

Abstract Model quantization is an important mechanism for energy-efficient deployment of
deep neural networks on resource-constrained devices by reducing the bit precision of …