From the 1 of 6 linked papers with an AI index.
6 papers
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…
TetriServe: Efficiently Serving Mixed DiT Workloads
Runyu Lu, Shiqi He, Wenxuan Tan +5
Diffusion Transformer (DiT) models excel at generating high-quality images through iterative denoising steps, but serving them under strict Service Level Objectives (SLOs) is chall…
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…
A stochastic gradient algorithm for non-separable optimization with convergence guarantee
Yingzhou Li, Ruofan Wu
We study non-separable objectives in which the loss depend on dataset-level quantities. We introduce an SGD-style framework that employs two batch-gradient constructs: the ideal pe…
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…
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…