Rescaled ASGD recovers convergence to the true global objective by rescaling worker stepsizes proportional to computation times, matching the known time lower bound in the leading term under non-convex smoothness and bounded heterogeneity.
Measuring the environmental impact of delivering AI at Google Scale
10 Pith papers cite this work, alongside 4 external citations. Polarity classification is still indexing.
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4
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OpenG2G is a new extensible simulation platform that lets users implement and compare classic, optimization, and learning-based controllers for AI datacenter power flexibility coordinated with the grid.
Larger batch sizes for LLM dialogue coding in healthcare simulations improve speed and reduce energy consumption while decreasing coding accuracy compared to human labels.
ICT LCAs form error-prone model systems requiring explicit dependency graphs and open versioned repositories for credible assessments.
The paper proposes a transparent proxy framework for estimating LLM inference and training environmental impacts from natural-language application descriptions.
An adapted scaling law predicts GPU energy consumption for diffusion model inference with R² > 0.9 within architectures and strong cross-architecture generalization.
The paper calls for life cycle assessment to capture embodied hardware costs and full pipeline operational costs in AI development and deployment.
A framework for two-tier AI service subscriptions that offer discounts for accepting lower quality or higher latency inference to reduce carbon emissions during high-intensity periods.
A review of AI sustainability studies finds inconsistent life cycle definitions and predominant reliance on coarse CO2e proxies, with limited coverage of water, materials, and multi-impact assessments.
AgentStop uses execution signals to early-terminate failing local LLM agent trajectories, cutting energy use 15-20% with minimal utility loss.
citing papers explorer
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Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity
Rescaled ASGD recovers convergence to the true global objective by rescaling worker stepsizes proportional to computation times, matching the known time lower bound in the leading term under non-convex smoothness and bounded heterogeneity.
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OpenG2G: A Simulation Platform for AI Datacenter-Grid Runtime Coordination
OpenG2G is a new extensible simulation platform that lets users implement and compare classic, optimization, and learning-based controllers for AI datacenter power flexibility coordinated with the grid.
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Scalable LLM-based Coding of Dialogue in Healthcare Simulation: Balancing Coding Performance, Processing Time, and Environmental Impact
Larger batch sizes for LLM dialogue coding in healthcare simulations improve speed and reduce energy consumption while decreasing coding accuracy compared to human labels.
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All LCA models are wrong. Are some of them useful? Towards open computational LCA in ICT
ICT LCAs form error-prone model systems requiring explicit dependency graphs and open versioned repositories for credible assessments.
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Transparent Screening for LLM Inference and Training Impacts
The paper proposes a transparent proxy framework for estimating LLM inference and training environmental impacts from natural-language application descriptions.
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Energy Scaling Laws for Diffusion Models: Quantifying Compute in Image Generation
An adapted scaling law predicts GPU energy consumption for diffusion model inference with R² > 0.9 within architectures and strong cross-architecture generalization.
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Evaluation of ML Resource Utilization Requires Model Life Cycle Assessment
The paper calls for life cycle assessment to capture embodied hardware costs and full pipeline operational costs in AI development and deployment.
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Greening AI Inference with Accuracy and Latency-aware User Incentives
A framework for two-tier AI service subscriptions that offer discounts for accepting lower quality or higher latency inference to reduce carbon emissions during high-intensity periods.
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From Cradle to Cloud: A Life Cycle Review of AI's Environmental Footprint
A review of AI sustainability studies finds inconsistent life cycle definitions and predominant reliance on coarse CO2e proxies, with limited coverage of water, materials, and multi-impact assessments.
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AgentStop: Terminating Local AI Agents Early to Save Energy in Consumer Devices
AgentStop uses execution signals to early-terminate failing local LLM agent trajectories, cutting energy use 15-20% with minimal utility loss.