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

In this community review report, we discuss applications and techniques for fast machine
learning (ML) in science—the concept of integrating powerful ML methods into the real-time …

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

Generative Large Language Models (LLMs) have demonstrated remarkable results for a
wide range of tasks. However, deploying these models for inference has been a significant …

Lung cancer, also known as pulmonary cancer, is one of the deadliest cancers, but yet
curable if detected at the early stage. At present, the ambiguous features of the lung cancer …

Transformer-based language models have become a key building block for natural
language processing. While these models are extremely accurate, they can be too large and …

Recent advances in state-of-the-art DNN architecture design have been moving toward
Transformer models. These models achieve superior accuracy across a wide range of …

Quantization of deep neural networks (DNN) has been proven effective for compressing and
accelerating DNN models. Data-free quantization (DFQ) is a promising approach without the …

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

Recent advances in machine learning by deep neural networks are significant. But using
these networks has been accompanied by a huge number of parameters for storage and …