[HTML][HTML] Energy efficiency in cloud computing data centers: a survey on software technologies
Cloud computing is a commercial and economic paradigm that has gained traction since
2006 and is presently the most significant technology in IT sector. From the notion of cloud …
2006 and is presently the most significant technology in IT sector. From the notion of cloud …
[HTML][HTML] Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning
R Desislavov, F Martínez-Plumed… - … Informatics and Systems, 2023 - Elsevier
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 …
exponential growth in the number of parameters. There are many studies corroborating …
Carbontracker: Tracking and predicting the carbon footprint of training deep learning models
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 …
often comes at the cost of training models for extensive periods on specialized hardware …
Characterization and prediction of deep learning workloads in large-scale gpu datacenters
Modern GPU datacenters are critical for delivering Deep Learning (DL) models and services
in both the research community and industry. When operating a datacenter, optimization of …
in both the research community and industry. When operating a datacenter, optimization of …
Coordinated batching and DVFS for DNN inference on GPU accelerators
SM Nabavinejad, S Reda… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
Employing hardware accelerators to improve the performance and energy-efficiency of DNN
applications is on the rise. One challenge of using hardware accelerators, including the GPU …
applications is on the rise. One challenge of using hardware accelerators, including the GPU …
Technology prospects for data-intensive computing
K Akarvardar, HSP Wong - Proceedings of the IEEE, 2023 - ieeexplore.ieee.org
For many decades, progress in computing hardware has been closely associated with
CMOS logic density, performance, and cost. As such, slowdown in 2-D scaling, frequency …
CMOS logic density, performance, and cost. As such, slowdown in 2-D scaling, frequency …
Fusionai: Decentralized training and deploying llms with massive consumer-level gpus
The rapid growth of memory and computation requirements of large language models
(LLMs) has outpaced the development of hardware, hindering people who lack large-scale …
(LLMs) has outpaced the development of hardware, hindering people who lack large-scale …
Benchmarking the performance and energy efficiency of AI accelerators for AI training
Deep learning has become widely used in complex AI applications. Yet, training a deep
neural network (DNNs) model requires a considerable amount of calculations, long running …
neural network (DNNs) model requires a considerable amount of calculations, long running …
An experimental study of reduced-voltage operation in modern FPGAs for neural network acceleration
We empirically evaluate an undervolting technique, ie, underscaling the circuit supply
voltage below the nominal level, to improve the power-efficiency of Convolutional Neural …
voltage below the nominal level, to improve the power-efficiency of Convolutional Neural …
{EnvPipe}: Performance-preserving {DNN} training framework for saving energy
Energy saving is a crucial mission for data center providers. Among many services, DNN
training and inference are significant contributors to energy consumption. This work focuses …
training and inference are significant contributors to energy consumption. This work focuses …