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Found 6 papers, 1 papers with code
A Configurable Pythonic Data Center Model for Sustainable Cooling and ML Integration
There have been growing discussions on estimating and subsequently reducing the operational carbon footprint of enterprise data centers.
Sustainability of Data Center Digital Twins with Reinforcement Learning
To tackle this, we've developed DCRL-Green, a multi-agent RL environment that empowers the ML community to design data centers and research, develop, and refine RL controllers for carbon footprint reduction in DCs.
Carbon Footprint Reduction for Sustainable Data Centers in Real-Time
As machine learning workloads significantly increase energy consumption, sustainable data centers with low carbon emissions are becoming a top priority for governments and corporations worldwide.
N-Critics: Self-Refinement of Large Language Models with Ensemble of Critics
We propose a self-correction mechanism for Large Language Models (LLMs) to mitigate issues such as toxicity and fact hallucination.
RTDK-BO: High Dimensional Bayesian Optimization with Reinforced Transformer Deep kernels
In this paper, we combine recent developments in Deep Kernel Learning (DKL) and attention-based Transformer models to improve the modeling powers of GP surrogates with meta-learning.
PyDCM: Custom Data Center Models with Reinforcement Learning for Sustainability
The increasing global emphasis on sustainability and reducing carbon emissions is pushing governments and corporations to rethink their approach to data center design and operation.
