Energy consumption has been widely studied in the computer architecture field for decades.
While the adoption of energy as a metric in machine learning is emerging, the majority of …

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 …

Accelerated edge devices, like Nvidia's Jetson with 1000+ CUDA cores, are increasingly
used for DNN training and federated learning, rather than just for inferencing workloads. A …

There is a huge demand for on-device execution of deep learning algorithms on mobile and
embedded platforms. These devices present constraints on the application due to limited …

The automation strategy of today's smart cities relies on large IoT (internet of Things)
systems that collect big data analytics to gain insights. Although there have been recent …

Deep learning algorithms have taken learning-based applications by storm because of their
algorithmic accuracy in modeling complex patterns, classification tasks and prediction …

Improving the capabilities of embedded devices and accelerators for Deep Neural Networks
(DNN) leads to a shift from cloud to edge computing. Especially for battery-powered …

This article presents PreVIous, a methodology to predict the performance of convolutional
neural networks (CNNs) in terms of throughput and energy consumption on vision-enabled …

Deep Learning (DL) is moving towards deploying workloads not only in cloud datacenters,
but also to the local devices. Although these are mostly limited to inference tasks, it still …

Deep learning has achieved accuracy and fast training speed and has been successfully
applied to many fields, including speech recognition, text processing, image processing and …