Currently, with the rapid development of deep learning, deep neural networks (DNNs) have
been widely applied in various computer vision tasks. However, in the pursuit of …

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 …

Quantization is an effective method for reducing memory footprint and inference time of
Neural Networks. However, ultra low precision quantization could lead to significant …

Neural network quantization enables the deployment of large models on resource-
constrained devices. Current post-training quantization methods fall short in terms of …

Quantization enables efficient acceleration of deep neural networks by reducing model
memory footprint and exploiting low-cost integer math hardware units. Quantization maps …

Neural network quantization is a promising compression technique to reduce memory
footprint and save energy consumption, potentially leading to real-time inference. However …

Recent advances in deep learning methods such as LLMs and Diffusion models have
created a need for improved quantization methods that can meet the computational …

Energy efficiency is critical for running computer vision on battery-powered systems, such as
mobile phones or UAVs (unmanned aerial vehicles, or drones). This book collects the …

A growing number of applications depend on Machine Learning (ML) functionality and
benefits from both higher quality ML predictions and better timeliness (latency) at the same …