9 citations · 24 across the 16 of their papers we have counts for
9 papers · 1 filter
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…
Energy Use of AI Inference, Efficiency Pathways, and Test-Time Scaling
Felipe Oviedo, Fiodar Kazhamiaka, Esha Choukse +5
As AI inference scales to billions of queries, estimates of per-query energy use are increasingly important for capacity planning, efficiency interventions, and policy. Yet many pu…
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…
Coach: Exploiting Temporal Patterns for All-Resource Oversubscription in Cloud Platforms
Benjamin Reidys, Pantea Zardoshti, Íñigo Goiri +16
Cloud platforms remain underutilized despite multiple proposals to improve their utilization (e.g., disaggregation, harvesting, and oversubscription). Our characterization of the r…
Cloud abstractions for AI workloads
Marco Canini, Theophilus A. Benson, Ricardo Bianchini +4
AI workloads, often hosted in multi-tenant cloud environments, require vast computational resources but suffer inefficiencies due to limited tenant-provider coordination. Tenants l…