18 citations · 28 across the 13 of their papers we have counts for
14 papers
Energy Calculus: A Compositional Algebra of Energy in Computational Systems
Mosharaf Chowdhury, Jae-Won Chung, Jeff J. Ma +2
Energy is a binding constraint for AI scaling, yet it lacks the formal treatment that computation, communication, and learning have long enjoyed. Recent systems demonstrate large e…
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…
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…
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…
GPU-to-Grid: Voltage Regulation via GPU Utilization Control
Zhirui Liang, Jae-Won Chung, Mosharaf Chowdhury +2
While the rapid expansion of data centers poses challenges for power grids, it also offers new opportunities as flexible loads. Existing power system research often abstracts data…
Where Do the Joules Go? Diagnosing Inference Energy Consumption
Jae-Won Chung, Ruofan Wu, Jeff J. Ma +1
Energy is now a critical ML computing resource. While measuring energy consumption and observing trends is a valuable first step, accurately understanding and diagnosing why those…
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…
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…
The ML.ENERGY Benchmark: Toward Automated Inference Energy Measurement and Optimization
Jae-Won Chung, Jeff J. Ma, Ruofan Wu +5
As the adoption of Generative AI in real-world services grow explosively, energy has emerged as a critical bottleneck resource. However, energy remains a metric that is often overl…
Evaluation Framework for AI Systems in "the Wild"
Sarah Jabbour, Trenton Chang, Anindya Das Antar +13
Generative AI (GenAI) models have become vital across industries, yet current evaluation methods have not adapted to their widespread use. Traditional evaluations often rely on ben…
Andes: Defining and Enhancing Quality-of-Experience in LLM-Based Text Streaming Services
Jiachen Liu, Jae-Won Chung, Zhiyu Wu +3
Large language models (LLMs) are now at the core of conversational AI services such as real-time translation and chatbots, which provide live user interaction by incrementally stre…
Toward Cross-Layer Energy Optimizations in AI Systems
Jae-Won Chung, Nishil Talati, Mosharaf Chowdhury
The "AI for Science, Energy, and Security" report from DOE outlines a significant focus on developing and optimizing artificial intelligence workflows for a foundational impact on…
Reducing Energy Bloat in Large Model Training
Jae-Won Chung, Yile Gu, Insu Jang +3
Training large AI models on numerous GPUs consumes a massive amount of energy, making power delivery one of the largest limiting factors in building and operating datacenters for A…
Chasing Low-Carbon Electricity for Practical and Sustainable DNN Training
Zhenning Yang, Luoxi Meng, Jae-Won Chung +1
Deep learning has experienced significant growth in recent years, resulting in increased energy consumption and carbon emission from the use of GPUs for training deep neural networ…