arXiv：cs.AI· Samuel Kushnir, Kavya Sreedhar, Yeshwanth Reddy Pogula, Amir Yazdanbakhsh, Narges Shahidi, Ming Liu, Varun Gohil, Ravi Iyer, Parthasarathy Ranganathan, Christina Delimitrou, Suvinay Subramanian·· 4 小时前AI 评分33
Coco：面向硬件-软件协同设计生命周期的智能体 Copilot
Coco: An Agentic Copilot for the Hardware--Software Co-Design Lifecycle
AI 导读
谷歌团队提出智能体平台 Coco（Copilot for Codesign），已部署于 TPU 架构师团队，用于加速实验搭建、模拟器扫描与洞察提取的协同设计流程。
正文
Authors:Samuel Kushnir, Kavya Sreedhar, Yeshwanth Reddy Pogula, Amir Yazdanbakhsh, Narges Shahidi, Ming Liu, Varun Gohil, Ravi Iyer, Parthasarathy Ranganathan, Christina Delimitrou, Suvinay Subramanian
Abstract:Co-designing ML models and the accelerators that run them is an unusual reasoning task: architects must draw confident, high-stakes conclusions about systems that do not yet exist, and the pace of both model evolution and hardware cadence means the analysis burden grows every quarter. The evidence behind each decision--hundreds of gigabytes of fresh simulation sweeps over novel design points--is by construction absent from any LLM's pretraining corpus, and there is no external literature to retrieve; naive "chat-with-your-data" approaches hallucinate exactly where correctness matters most. We present Coco (Copilot for Codesign), an agentic platform deployed with TPU architects that accelerates the co-design lifecycle of setting up experiments, sweeping simulators, and deriving insights. Coco is built as four layers: (i) a datastore that automatically registers every simulation sweep into a normalized relational schema, so agents ground every number in a SQL query rather than scraping heterogeneous files; (ii) a library of tools with typed APIs that agents compose without human orchestration; (iii) agents that encode recurring analysis workflows--most notably iso-execution analysis, which compares systems at matched execution configurations, including swept-but-dominated points off the Pareto frontier; and (iv) a platform UX whose navigation state doubles as agent context. We report early deployment experience toward a reduction in time-to-simulation and time-to-insight, and argue that co-design is a distinct agentic domain: its data must be retrieved rather than memorized, its workflows are recurring but context-dependent, and expert adoption hinges on UX that balances IDE-style control with interactive exploration.
| Subjects: | Programming Languages (cs.PL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.02376 [cs.PL] |
| (or arXiv:2610.02376v1 [cs.PL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.02376 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Samuel Kushnir [view email]
[v1]
Thu, 1 Oct 2026 18:55:52 UTC (15 KB)
来源：arXiv：cs.AI · arxiv.org