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Found 21 papers, 9 papers with code
Andes: Defining and Enhancing Quality-of-Experience in LLM-Based Text Streaming Services
The advent of large language models (LLMs) has transformed text-based services, enabling capabilities ranging from real-time translation to AI-driven chatbots.
FedTrans: Efficient Federated Learning Over Heterogeneous Clients via Model Transformation
Federated learning (FL) aims to train machine learning (ML) models across potentially millions of edge client devices.
Toward Cross-Layer Energy Optimizations in Machine Learning Systems
The enormous energy consumption of machine learning (ML) and generative AI workloads shows no sign of waning, taking a toll on operating costs, power delivery, and environmental sustainability.
Venn: Resource Management Across Federated Learning Jobs
Scheduling edge resources among multiple FL jobs is different from GPU scheduling for cloud ML because of the ephemeral nature and planetary scale of participating devices as well as the overlapping resource requirements of diverse FL jobs.
Perseus: Removing Energy Bloat from Large Model Training
Training large AI models on numerous GPUs consumes a massive amount of energy.
Efficient Large Language Models: A Survey
Large Language Models (LLMs) have demonstrated remarkable capabilities in important tasks such as natural language understanding, language generation, and complex reasoning and have the potential to make a substantial impact on our society.
Oobleck: Resilient Distributed Training of Large Models Using Pipeline Templates
Oobleck enables resilient distributed training of large DNN models with guaranteed fault tolerance.
Chasing Low-Carbon Electricity for Practical and Sustainable DNN Training
Specifically, our solution observes real-time carbon intensity shifts during training and controls the energy consumption of GPUs, thereby reducing carbon footprint while maintaining training performance.
FLINT: A Platform for Federated Learning Integration
Cross-device federated learning (FL) has been well-studied from algorithmic, system scalability, and training speed perspectives.
Packing Privacy Budget Efficiently
This DP budget can be regarded as a new type of compute resource in workloads of multiple ML models training on user data.
Auxo: Efficient Federated Learning via Scalable Client Clustering
In this paper, we explore an additional layer of complexity to mitigate such heterogeneity by grouping clients with statistically similar data distributions (cohorts).
Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training
In this paper, we observe that common practices to improve training performance can often lead to inefficient energy usage.
Swan: A Neural Engine for Efficient DNN Training on Smartphone SoCs
The need to train DNN models on end-user devices (e. g., smartphones) is increasing with the need to improve data privacy and reduce communication overheads.
Egeria: Efficient DNN Training with Knowledge-Guided Layer Freezing
To explore this, we first introduce the notion of training plasticity to quantify the training progress of internal DNN layers.
Treehouse: A Case For Carbon-Aware Datacenter Software
The end of Dennard scaling and the slowing of Moore's Law has put the energy use of datacenters on an unsustainable path.
The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning
The Internet of Things (IoT) is on the verge of a major paradigm shift.
Fed-ensemble: Improving Generalization through Model Ensembling in Federated Learning
In this paper we propose Fed-ensemble: a simple approach that bringsmodel ensembling to federated learning (FL).
FedScale: Benchmarking Model and System Performance of Federated Learning at Scale
We present FedScale, a federated learning (FL) benchmarking suite with realistic datasets and a scalable runtime to enable reproducible FL research.
Oort: Efficient Federated Learning via Guided Participant Selection
In this paper, we propose Oort to improve the performance of federated training and testing with guided participant selection.
Salus: Fine-Grained GPU Sharing Primitives for Deep Learning Applications
We show that these primitives can then be used to implement flexible sharing policies such as fairness, prioritization, and packing for various use cases.
Fast and Accurate Performance Analysis of LTE Radio Access Networks
Our choice of this domain is influenced by its commonalities with several other domains that produce real-time data, our access to a large live dataset, and their real-time nature and dimensionality which makes it a natural fit for a popular analysis technique, machine learning (ML).
