Monitoring, understanding, and optimizing the energy consumption of Machine Learning
(ML) are various reasons why it is necessary to evaluate the energy usage of ML. However …

Given the success of the gated recurrent unit, a natural question is whether all the gates of
the long short-term memory (LSTM) network are necessary. Previous research has shown …

With promising benefits of splitting deep neural network (DNN) computation loads to the
edge server, split computing has been a novel paradigm achieving high-quality artificial …

Estimating the energy and memory consumption of machine learning (ML) models for
intrusion detection ensures efficient allocation of system resources. This study investigates …

The number of mobile devices (smartphone, tablet, laptop, etc.) and Internet users are
continually increasing. Due to the accessibility provided by cloud computing, Internet and …

The heat generation from recent advanced computer chips is increasing rapidly. This
creates a challenge in cooling the chips while maintaining their temperatures below the …

Deep neural networks (DNNs) involving massive neural nodes grouped into different neural
layers have been a promising innovation for function approximation and inference, which …

Energy efficiency is a major topic in every scientific field, since being energy efficient means
producing more for a smaller cost. Data centers are no exception to this rule as their energy …

Splitting the computation loads of a neural network (NN) training task to multiple stations,
split computing has been the most promising technology to sustain high-accuracy model for …

The recent progress in nanotechnologies is giving birth to a novel topology of wireless
networks characterized by a high local density and an intensive node instability such as in …