With the ever‐growing adoption of artificial intelligence (AI)‐based systems, the carbon
footprint of AI is no longer negligible. AI researchers and practitioners are therefore urged to …

With the adoption of smart systems, artificial neural networks (ANNs) have become
ubiquitous. Conventional ANN implementations have high energy consumption, limiting …

The progress of some AI paradigms such as deep learning is said to be linked to an
exponential growth in the number of parameters. There are many studies corroborating …

In order to realize the full potential of wireless edge artificial intelligence (AI), very large and
diverse datasets will often be required for energy-demanding model training on resource …

In the past few decades, artificial intelligence (AI) technology has experienced swift
developments, changing everyone's daily life and profoundly altering the course of human …

Deep learning (DL) can achieve impressive results across a wide variety of tasks, but this
often comes at the cost of training models for extensive periods on specialized hardware …

The field of artificial intelligence has experienced a dramatic methodological shift towards
large neural networks trained on plentiful data. This shift has been fueled by recent …

Bioinspired spiking neural networks (SNNs), operating with asynchronous binary signals (or
spikes) distributed over time, can potentially lead to greater computational efficiency on …

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

The use of Artificial Intelligence (ai), and more specifically of Deep Learning (dl), in modern
software systems, is nowadays widespread and continues to grow. At the same time, its …