Queryable Microarchitecture Knowledge Base using Retrieval-Augmented Generation

Microarchitecture documentation, such as datasheets and user manuals, is indispensable for embedded software development. However, the extensive volume and complexity of these documents render information retrieval a time- and effort-intensive task. To address this challenge, we propose a framework for constructing a queryable knowledge base on microarchitecture documentation, leveraging Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs). As a proof of concept, we implement a knowledge base on AURIX TriCore TC27x documentation and evaluate this knowledge base by querying it with a curated set of questions. The generated responses are evaluated by measuring their semantic similarity to reference answers. In our evaluation, we assess the performance of six LLMs with different model architectures and sizes. The results show that the smaller models (with 8 billion and 3 billion parameters) achieve similarity scores comparable to those of the larger model (with 72 billion parameters). These initial findings demonstrate the robustness of our framework for creating queryable knowledge bases and the potential of smaller LLMs for efficient information retrieval in this context