Improving the Effectiveness of LLM-Assisted ISS Generation from Design Specification H/F

Job description: The internship aims to enhance the performance of large language models (LLMs) in generating Instruction Set Simulator (ISS) code by using Reinforcement Learning (RL) to optimize and automate prompt tuning. Additionally, the internship seeks to expand dataset coverage by using simulators such as QEMU (Quick Emulator) in-the-loop to simulate and evaluate a wider range of architectures, enabling access to diverse implementations and increasing dataset diversity. While RL will be the primary focus, alternative methods can also be explored throughout the internship. The main activities will involve using RL to dynamically adjust the prompts fed to the LLM, guiding it to improve code correctness, compilation success, and functional efficiency. Open questions for investigation include, but are not limited to, how to define rewards that balance compilability and functionality, how feedback from these rewards can be used to refine future prompts, and what strategies can effectively integrate RL rewards into the prompt generation process. The RL agent will iteratively adjust the prompt based on feedback from compilation and functional tests, using QEMU to assess the quality of the generated code. By simulating multiple architectures in the QEMU environment, the internship will aim to broaden the dataset coverage, making the model more adaptable to different hardware implementations. The results of this work have the potential to contribute significant insights into this field and may lead to publication in relevant conferences. During this internship, the student will gain practical experience with advanced AI techniques, such as RL and automatic prompt tuning, while enhancing their knowledge of LLM-based code generation. This project provides a valuable opportunity to develop key skills in AI-driven hardware design and to contribute to innovative research.

Level of qualification studied: Bac+5 - Master of Science

Languages: English Fluent