We review the problem of automating hardware-aware architectural design process of Deep
Neural Networks (DNNs). The field of Convolutional Neural Network (CNN) algorithm design …

Neural Architecture Search (NAS) methods have been growing in popularity. These
techniques have been fundamental to automate and speed up the time consuming and error …

This paper introduces EfficientNetV2, a new family of convolutional networks that have faster
training speed and better parameter efficiency than previous models. To develop these …

The growing carbon footprint of artificial intelligence (AI) models, especially large ones such
as GPT-3, has been undergoing public scrutiny. Unfortunately, however, the equally …

Transformer-based Deep Neural Network architectures have gained tremendous interest
due to their effectiveness in various applications across Natural Language Processing (NLP) …

The role of agricultural development is very important in the economy of a country. However,
the occurrence of several plant diseases is a major hindrance to the growth rate and quality …

The primary layer of protection for vital organs in the human body is the skin. It functions as a
barrier to protect our internal organs from different sources. However, infections caused by …

Neural architecture search (NAS) has attracted increasing attention. In recent years,
individual search methods have been replaced by weight-sharing search methods for higher …

Deep neural networks (DNN) are now dominating in the most challenging applications of
machine learning. As DNNs can have complex architectures with millions of trainable …

Prereflow automatic optical inspection (AOI) has been widely used to ensure product quality
in surface mount technology (SMT). When confronted with a complex industrial environment …