Energy Efficiency in Computing
82 papers on this topic
Splitwise: Collaborative Edge-Cloud Inference for LLMs via Lyapunov-Assisted DRL
Deploying large language models (LLMs) on edge devices is challenging due to their limited memory and power resources. Cloud-only inference reduces device burden but introduces high latency and...
Climate Implications of Diffusion-based Generative Visual AI Systems and their Mass Adoption
Climate implications of rapidly developing digital technologies, such as blockchains and the associated crypto mining and NFT minting, have been well documented and their massive GPU energy use...
Sustainable Supercomputing for AI: GPU Power Capping at HPC Scale
As research and deployment of AI grows, the computational burden to support and sustain its progress inevitably does too. To train or fine-tune state-of-the-art models in NLP, computer vision,...
Evaluating the Energy-Efficiency of the Code Generated by LLMs
As the quality of code generated by Large Language Models (LLMs) improves, their adoption in the software industry for automated code generation continues to grow. Researchers primarily focus on...
Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model
Progress in machine learning (ML) comes with a cost to the environment, given that training ML models requires significant computational resources, energy and materials. In the present article, we...
Sustainability of Data Center Digital Twins with Reinforcement Learning
The rapid growth of machine learning (ML) has led to an increased demand for computational power, resulting in larger data centers (DCs) and higher energy consumption. To address this issue and...
Energy and Policy Considerations for Deep Learning in NLP
Recent progress in hardware and methodology for training neural networks has ushered in a new generation of large networks trained on abundant data. These models have obtained notable gains in...
DVFO: Learning-Based DVFS for Energy-Efficient Edge-Cloud Collaborative Inference
Due to limited resources on edge and different characteristics of deep neural network (DNN) models, it is a big challenge to optimize DNN inference performance in terms of energy consumption and...
Resource-Efficient Generative AI Model Deployment in Mobile Edge Networks
The surging development of Artificial Intelligence-Generated Content (AIGC) marks a transformative era of the content creation and production. Edge servers promise attractive benefits, e.g.,...
AI Augmented Edge and Fog Computing: Trends and Challenges
In recent years, the landscape of computing paradigms has witnessed a gradual yet remarkable shift from monolithic computing to distributed and decentralized paradigms such as Internet of Things...
Reinforcement Learning-Based Adaptive Load Balancing for Dynamic Cloud Environments
Efficient load balancing is crucial in cloud computing environments to ensure optimal resource utilization, minimize response times, and prevent server overload. Traditional load balancing...
Dynamic Scheduling Strategies for Resource Optimization in Computing Environments
The rapid development of cloud-native architecture has promoted the widespread application of container technology, but the optimization problems in container scheduling and resource management...
Enhancing Energy-Awareness in Deep Learning through Fine-Grained Energy Measurement
With the increasing usage, scale, and complexity of Deep Learning (DL) models, their rapidly growing energy consumption has become a critical concern. Promoting green development and energy...
Hierarchical Multi-Agent Framework for Carbon-Efficient Liquid-Cooled Data Center Clusters
Reducing the environmental impact of cloud computing requires efficient workload distribution across geographically dispersed Data Center Clusters (DCCs) and simultaneously optimizing liquid and...
Fusion Intelligence for Digital Twinning AI Data Centers: A Synergistic GenAI-PhyAI Approach
The explosion in artificial intelligence (AI) applications is pushing the development of AI-dedicated data centers (AIDCs), creating management challenges that traditional methods and standalone...
Multi-objective Deep Reinforcement Learning for Mobile Edge Computing
Mobile edge computing (MEC) is essential for next-generation mobile network applications that prioritize various performance metrics, including delays and energy consumption. However, conventional...
Energy Efficiency of Training Neural Network Architectures: An Empirical Study
The evaluation of Deep Learning models has traditionally focused on criteria such as accuracy, F1 score, and related measures. The increasing availability of high computational power environments...
An Energy-Aware Approach to Design Self-Adaptive AI-based Applications on the Edge
The advent of edge devices dedicated to machine learning tasks enabled the execution of AI-based applications that efficiently process and classify the data acquired by the resource-constrained...