works on

From the 1 of 11 linked papers with an AI index.

activity
20242026
collaborators

11 papers

cs.DC2026

Energy Calculus: A Compositional Algebra of Energy in Computational Systems

Mosharaf Chowdhury, Jae-Won Chung, Jeff J. Ma +2

The paper introduces Energy Calculus, a compositional algebra that treats energy as a first‑class primitive, allowing systematic combination of energy measurements across sequentia…

cs.CL2026

The Language-Energy Divide: Measuring Energy Costs of Multilingual LLM Inference

Naihao Deng, Alissa Shen, Yiming Feng +5

Large language models (LLMs) are increasingly deployed in multilingual settings, yet the energy costs of serving these models across different languages remain poorly understood. W…

cs.LG2026

Kareus: Joint Reduction of Dynamic and Static Energy in Large Model Training

Ruofan Wu, Jae-Won Chung, Mosharaf Chowdhury

The computing demand of AI is growing at an unprecedented rate, but energy supply is not keeping pace. As a result, energy has become an expensive and contended resource that requi…

cs.LG2026

OpenG2G: A Simulation Platform for AI Datacenter-Grid Runtime Coordination

Jae-Won Chung, Zhirui Liang, Yanyong Mao +3

AI's growing compute demand and new datacenter buildouts present major capacity and reliability challenges for the electricity grid, leading to multi-year interconnection delays fo…

cs.LG2026

Cornserve: A Distributed Serving System for Any-to-Any Multimodal Models

Jae-Won Chung, Jeff J. Ma, Jisang Ahn +4

Any-to-Any models are an emerging class of multimodal models that accept combinations of multimodal data (e.g., text, image, video, audio) as input and generate them as output. Ser…

cs.LG2026

Cornfigurator: Automated Planning for Any-to-Any Multimodal Model Serving

Jeff J. Ma, Jae-Won Chung, Jisang Ahn +5

Any-to-Any models are an emerging class of multimodal models that accept combinations of text and multimodal data as input and generate them as output, introducing heterogeneous co…