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20222024
most citedUnderstanding Scaling Laws for Recommendation Models

6 citations · 20 across the 10 of their papers we have counts for

collaborators

10 papers

cs.LG2024

Beyond Efficiency: Scaling AI Sustainably

Carole-Jean Wu, Bilge Acun, Ramya Raghavendra +1

Barroso's seminal contributions in energy-proportional warehouse-scale computing launched an era where modern datacenters have become more energy efficient and cost effective than…

cs.LG20242 cited

CHAI: Clustered Head Attention for Efficient LLM Inference

Saurabh Agarwal, Bilge Acun, Basil Hosmer +5

Large Language Models (LLMs) with hundreds of billions of parameters have transformed the field of machine learning. However, serving these models at inference time is both compute…

cs.DC20235 cited

Carbon Responder: Coordinating Demand Response for the Datacenter Fleet

Jiali Xing, Bilge Acun, Aditya Sundarrajan +5

The increasing integration of renewable energy sources results in fluctuations in carbon intensity throughout the day. To mitigate their carbon footprint, datacenters can implement…

cs.LG2023

GEVO-ML: Optimizing Machine Learning Code with Evolutionary Computation

Jhe-Yu Liou, Stephanie Forrest, Carole-Jean Wu

Parallel accelerators, such as GPUs, are key enablers for large-scale Machine Learning (ML) applications. However, ML model developers often lack detailed knowledge of the underlyi…

cs.AR20232 cited

Design Space Exploration and Optimization for Carbon-Efficient Extended Reality Systems

Mariam Elgamal, Doug Carmean, Elnaz Ansari +8

As computing hardware becomes more specialized, designing environmentally sustainable computing systems requires accounting for both hardware and software parameters. Our goal is t…

cs.DC20232 cited

GreenScale: Carbon-Aware Systems for Edge Computing

Young Geun Kim, Udit Gupta, Andrew McCrabb +4

To improve the environmental implications of the growing demand of computing, future applications need to improve the carbon-efficiency of computing infrastructures. State-of-the-a…