{"ID":2851721,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.19577","arxiv_id":"2510.19577","title":"gem5 Co-Pilot: AI Assistant Agent for Architectural Design Space Exploration","abstract":"Generative AI is increasing the productivity of software and hardware development across many application domains. In this work, we utilize the power of Large Language Models (LLMs) to develop a co-pilot agent for assisting gem5 users with automating design space exploration. Computer architecture design space exploration is complex and time-consuming, given that numerous parameter settings and simulation statistics must be analyzed before improving the current design. The emergence of LLMs has significantly accelerated the analysis of long-text data as well as smart decision making, two key functions in a successful design space exploration task. In this project, we first build gem5 Co-Pilot, an AI agent assistant for gem5, which comes with a webpage-GUI for smooth user interaction, agent automation, and result summarization. We also implemented a language for design space exploration, as well as a Design Space Database (DSDB). With DSDB, gem5 Co-Pilot effectively implements a Retrieval Augmented Generation system for gem5 design space exploration. We experiment on cost-constraint optimization with four cost ranges and compare our results with two baseline models. Results show that gem5 Co-Pilot can quickly identify optimal parameters for specific design constraints based on performance and cost, with limited user interaction.","short_abstract":"Generative AI is increasing the productivity of software and hardware development across many application domains. In this work, we utilize the power of Large Language Models (LLMs) to develop a co-pilot agent for assisting gem5 users with automating design space exploration. Computer architecture design space explorat...","url_abs":"https://arxiv.org/abs/2510.19577","url_pdf":"https://arxiv.org/pdf/2510.19577v1","authors":"[\"Zuoming Fu\",\"Alex Manley\",\"Mohammad Alian\"]","published":"2025-10-22T13:28:36Z","proceeding":"cs.AR","tasks":"[\"cs.AR\"]","methods":"[\"Large Language Model\",\"Language Model\",\"LoRA\"]","has_code":false}