3 citations · 3 across the 3 of their papers we have counts for
13 papers
AtumAI: A Principled Framework for Agentic Generation of Datacenter Control-Plane Policies
Qiushi Lin, Chaojie Zhang, Ãñigo Goiri +3
The efficiency of a datacenter rests on its control plane policies. Designing these policies is increasingly hard: the hardware-software stack grows fast, the design space is vast…
Agentic Coding in the Wild: Characterizing GitHub Copilot Traces at Production Scale
Banruo Liu, Haoran Qiu, Ãñigo Goiri +3
AI coding agents like GitHub Copilot, Claude Code, and Codex interleave multi-step LLM inference with tool execution, creating a workload different from chatbots. We present the fi…
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…
Designing Datacenter Power Delivery Hierarchies for the AI Era
Grant Wilkins, Fiodar Kazhamiaka, Alok Gautam Kumbhare +2
Demand for AI accelerators is rapidly increasing rack power density, with projections approaching 1MW per deployment by 2027. This poses a major challenge for datacenter power deli…
StreamWise: Serving Multi-Modal Generation in Real-Time at Scale
Haoran Qiu, Gohar Irfan Chaudhry, Chaojie Zhang +4
Advances in multi-modal generative models are enabling new applications, from storytelling to automated media synthesis. Most current workloads generate simple outputs (e.g., image…
Sherlock: Reliable and Efficient Agentic Workflow Execution
Yeonju Ro, Haoran Qiu, Ãñigo 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…