Add to ORKGThe evaluation of Deep Learning (DL) models has traditionally focused on criteria such as accuracy, F1 score, and related measures. The increasing availability of high computational power environments allows the creation of deeper and more complex models. However, the computations needed to train such models entail a large carbon footprint. In this work, we study the relations between DL model architectures and their environmental impact in terms of energy consumed and CO2 emissions produced during training by means of an empirical study using Deep Convolutional Neural Networks. Concretely, we study: (i) the impact of the architecture and the location where the computations are hosted on the energy consumption and emissions produced; (ii) t...
Recently, there has been a trend of shifting the execution of deep learning inference tasks toward t...
Over the past decade, deep learning (DL) has led to significant advancements in various fields of ar...
Deep neural network models are commonly used in various real-life applications due to their high pre...
With the rapid development of machine learning, deep learning has demonstrated superior performance ...
International audienceWith the increasingly complex models used in machine learning and the large am...
International audienceThe training energy efficiency of deep neural networks became an extensively s...
Deep Neural Networks (DNN) has transformed the automation of a wide range of industries and finds in...
Deep learning has produced some of the most accurate and most versatile techniques for many applicat...
In this work, we look at the intersection of Sustainable Software Engineering and AI engineering kno...
Deep learning and artificial intelligence are often viewed as panacea technologies — ones which can ...
[EN] In the last decade, deep learning has achieved spectacular results in numerous applications. Th...
While providing the same functionality, the various Deep Learning software frameworks available thes...
Energy consumption has been widely studied in the computer architecture field for decades. While the...
Deep convolutional neural networks (CNNs) are indispensable to state-of-the-art computer vision ...
International audienceThis paper contributes towards better understanding the energy consumption tra...
Recently, there has been a trend of shifting the execution of deep learning inference tasks toward t...
Over the past decade, deep learning (DL) has led to significant advancements in various fields of ar...
Deep neural network models are commonly used in various real-life applications due to their high pre...
With the rapid development of machine learning, deep learning has demonstrated superior performance ...
International audienceWith the increasingly complex models used in machine learning and the large am...
International audienceThe training energy efficiency of deep neural networks became an extensively s...
Deep Neural Networks (DNN) has transformed the automation of a wide range of industries and finds in...
Deep learning has produced some of the most accurate and most versatile techniques for many applicat...
In this work, we look at the intersection of Sustainable Software Engineering and AI engineering kno...
Deep learning and artificial intelligence are often viewed as panacea technologies — ones which can ...
[EN] In the last decade, deep learning has achieved spectacular results in numerous applications. Th...
While providing the same functionality, the various Deep Learning software frameworks available thes...
Energy consumption has been widely studied in the computer architecture field for decades. While the...
Deep convolutional neural networks (CNNs) are indispensable to state-of-the-art computer vision ...
International audienceThis paper contributes towards better understanding the energy consumption tra...
Recently, there has been a trend of shifting the execution of deep learning inference tasks toward t...
Over the past decade, deep learning (DL) has led to significant advancements in various fields of ar...
Deep neural network models are commonly used in various real-life applications due to their high pre...