1 citations · 2 across the 4 of their papers we have counts for
8 papers
Sherlock: Reliable and Efficient Agentic Workflow Execution
Yeonju Ro, Haoran Qiu, Íñigo Goiri +6
With the increasing adoption of large language models (LLM), agentic workflows, which compose multiple LLM calls with tools, retrieval, and reasoning steps, are increasingly replac…
Rearchitecting Datacenter Lifecycle for AI: A TCO-Driven Framework
Jovan Stojkovic, Chaojie Zhang, Íñigo Goiri +1
The rapid rise of large language models (LLMs) has been driving an enormous demand for AI inference infrastructure, mainly powered by high-end GPUs. While these accelerators offer…
Murakkab: Resource-Efficient Agentic Workflow Orchestration in Cloud Platforms
Gohar Irfan Chaudhry, Esha Choukse, Haoran Qiu +4
Agentic workflows commonly coordinate multiple models and tools with complex control logic. They are quickly becoming the dominant paradigm for AI applications. However, serving th…
Power Stabilization for AI Training Datacenters
Esha Choukse, Brijesh Warrier, Scot Heath +54
Large Artificial Intelligence (AI) training workloads spanning several tens of thousands of GPUs present unique power management challenges. These arise due to the high variability…
TAPAS: Thermal- and Power-Aware Scheduling for LLM Inference in Cloud Platforms
Jovan Stojkovic, Chaojie Zhang, Íñigo Goiri +5
The rising demand for generative large language models (LLMs) poses challenges for thermal and power management in cloud datacenters. Traditional techniques often are inadequate fo…
Towards Resource-Efficient Compound AI Systems
Gohar Irfan Chaudhry, Esha Choukse, Íñigo Goiri +3
Compound AI Systems, integrating multiple interacting components like models, retrievers, and external tools, have emerged as essential for addressing complex AI tasks. However, cu…