1 citations · 1 across the 5 of their papers we have counts for
5 papers
Offline Energy-Optimal LLM Serving: Workload-Based Energy Models for LLM Inference on Heterogeneous Systems
Grant Wilkins, Srinivasan Keshav, Richard Mortier
The rapid adoption of large language models (LLMs) has led to significant advances in natural language processing and text generation. However, the energy consumed through LLM mode…
Hybrid Heterogeneous Clusters Can Lower the Energy Consumption of LLM Inference Workloads
Grant Wilkins, Srinivasan Keshav, Richard Mortier
Both the training and use of Large Language Models (LLMs) require large amounts of energy. Their increasing popularity, therefore, raises critical concerns regarding the energy eff…
Global, robust and comparable digital carbon assets
Sadiq Jaffer, Michael Dales, Patrick Ferris +5
Carbon credits purchased in the voluntary carbon market allow unavoidable emissions, such as from international flights for essential travel, to be offset by an equivalent climate…
From Spectra to Biophysical Insights: End-to-End Learning with a Biased Radiative Transfer Model
Yihang She, Clement Atzberger, Andrew Blake +1
Advances in machine learning have boosted the use of Earth observation data for climate change research. Yet, the interpretability of machine-learned representations remains a chal…
Cohesive Powers of Structures
Valentina Harizanov, Keshav Srinivasan
A cohesive power of a structure is an effective analog of the classical ultrapower of a structure. We start with a computable structure, and consider its countable ultrapower over…