4 citations · 4 across the 5 of their papers we have counts for
7 papers
Efficiency Will Not Lead to Sustainable Reasoning AI
Philipp Wiesner, Daniel W. O'Neill, Francesca Larosa +1
AI research is increasingly moving toward complex problem solving, where models are optimized not only for pattern recognition but for multi-step reasoning. Historically, computing…
What happens when nanochat meets DiLoCo?
Alexander Acker, Soeren Becker, Sasho Nedelkoski +3
Although LLM training is typically centralized with high-bandwidth interconnects and large compute budgets, emerging methods target communication-constrained training in distribute…
Optimizing Microgrid Composition for Sustainable Data Centers
Julius Irion, Philipp Wiesner, Jonathan Bader +1
As computing energy demand continues to grow and electrical grid infrastructure struggles to keep pace, an increasing number of data centers are being planned with colocated microg…
Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations
Miray Özcan, Philipp Wiesner, Philipp Weiß +1
The environmental impact of Large Language Models (LLMs) is rising significantly, with inference now accounting for more than half of their total lifecycle carbon emissions. Howeve…
Moving Beyond Marginal Carbon Intensity: A Poor Metric for Both Carbon Accounting and Grid Flexibility
Philipp Wiesner, Odej Kao
Marginal Carbon Intensity (MCI) has been promoted as an effective metric for carbon-aware computing. Although it is already considered as impractical for carbon accounting purposes…
Choosing the Right Battery Model for Data Center Simulations
Paul Kilian, Philipp Wiesner, Odej Kao
As demand for computing resources continues to rise, the increasing cost of electricity and anticipated regulations on carbon emissions are prompting changes in data center power s…