Domain-specific hardware is becoming a promising topic in the backdrop of improvement
slow down for general-purpose processors due to the foreseeable end of Moore's Law …

With the emergence of a spectrum of high-end mobile devices, many applications that
formerly required desktop-level computation capability are being transferred to these …

Structured weight pruning is a representative model compression technique of DNNs to
reduce the storage and computation requirements and accelerate inference. An automatic …

Vision-based fire detection systems have been significantly improved by deep models;
however, higher numbers of false alarms and a slow inference speed still hinder their …

Deep neural network (DNN) models typically have many hyperparameters that can be
configured to achieve optimal performance on a particular dataset. Practitioners usually tune …

Over the past decades, fire has been considered one of the most serious natural disasters
because of its devastating nature, rapid spread, and high impact on the ecology, economy …

Deep neural networks (DNNs) are being prototyped for a variety of artificial intelligence (AI)
tasks including computer vision, data analytics, robotics, etc. The efficacy of DNNs coincides …

Convolutional neural networks (CNNs) have been widely used in image classification and
recognition due to their effectiveness; however, CNNs use a large volume of weight data that …

Abstract Model quantization has emerged as a mandatory technique for efficient inference
with advanced Deep Neural Networks (DNN). It converts the model parameters in full …

Over the last decade, machine learning (ML) and deep learning (DL) algorithms have
significantly evolved and been employed in diverse applications, such as computer vision …