Abstract: DNN accelerators are often developed and evaluated in isolation without considering the cross-stack, system-level effects in real-world environments. This makes it difficult to appreciate ...

Recently Convolution Neural Network (CNN) has become very popular in AI applications. CNN requires a lot of computational resources that has significant impact on chip size. In this paper we proposed ...

Inspired by the face covering period in the past two years, COVID-19 pandemic has resulted in the mandate of public safety measures such as face mask-wearing in many countries. This paper provides a ...

This article studies the role of architecture design, i.e. choice of the number of nodes at each hidden layer, in deep neural networks (DNNs). We give a theoretical explanation that invariance and ...

Breast cancer detection through mammograms is very crucial for early diagnosis, and tumor segmentation is an important step in treatment planning. This paper proposes a novel approach by combining ...

An Internet of Things (IoT) platform is a software architecture that enables the connection, management, and analysis of IoT devices, sensors, and data. It provides a centralized system for IoT ...

Interpreting a deep Convolutional Neural Network (CNN) involves identifying the features in a hierarchy of layers that contribute to recognition. Although the current approaches serve as methods to ...

This paper proposes a new convolutional neuronal network architecture for pneumonia detection in pediatric healthcare. The model was constructed from the scratch and was trained, validated, and tested ...

The implications for enterprise AI are significant. Until recently, most leading systems were only available through closed ...

Perceptual image hashing has emerged as a crucial forensic tool within the Internet of Things (IoT) ecosystem. Traditional perceptual hashing algorithms predominantly rely on global image features to ...

With the widespread application of deep learning and Convolutional Neural Networks (CNN) in image classification, how to effectively improve model performance and reduce its complexity has become a ...

Intelligent machine fault diagnosis methods that leverage machine learning techniques have received widespread attention owing to their proven efficacy in enhancing production efficiency and quality ...