This research addresses the challenges faced by firmware developers, security researchers, and enthusiasts who work with low-level microcontroller (MCU) documentation, which often spans hundreds of complex pages. Current structured approaches, such as System View Description (SVD), are widely used but suffer from manual, labor-intensive creation processes and inconsistent vendor adherence to CMSIS-SVD standards. We propose an automated solution using Large Language Models (LLMs) integrated with Retrieval-Augmented Generation (RAG), capable of effectively parsing and extracting structured information, including text, tables, and images from MCU reference manuals/ datasheets. To mitigate hallucination issues inherent in LLMs, we fine-tuned models using a dataset derived from CMSIS-SVD files, which we will open-source for community benefit. We also experimented with few-shot models. Additionally, we developed a standardized structured ontology that is automatically populated with information extracted through LLM assistance from the reference manuals of the corresponding MCUs. Our approach was evaluated using OpenAI's GPT-4o under one-shot, few-shot, and fine-tuning scenarios, all incorporating RAG. We also experimented with the open-source LLM model CodeLlama. The results highlight substantial improvements in automatically extracting peripheral details and information from MCU reference manuals. Thus, it helps reduce manual effort and time. The key contribution of our work lies in the tailored adaptation of existing AI techniques to address the specific challenges of embedded systems documentation. We perform standardized ontology creation and multimodal parsing. We leverage RAG with MCU-specific finetuning and few-shot learning to generate structured information from hundreds of pages of MCU documentation. This opens the door to potential applications such as more accurate firmware code generation and reverse engineering for security analysis.

The proliferation of Automatic Speech Recognition (ASR) systems has revolutionized translation and transcription. However, challenges persist in ensuring inclusive communication for non-native English speakers. This study quantifies the gap between accented and native English speech using Wav2Vec 2.0, a state-of-the-art transformer model. Notably, we found that accented speech exhibits significantly higher word error rates of 30-50%, in contrast to native speakers’ 2-8% (Baevski et al. 2020). Our exploration extends to leveraging accessible online datasets to highlight the potential of enhancing speech recognition by fine-tuning the Wav2Vec 2.0 model. Through experimentation and analysis, we highlight the challenges with training models on accented speech. By refining models and addressing data quality issues, our work presents a pipeline for future investigations aimed at developing an integrated system capable of effectively engaging with a broader range of individuals with diverse backgrounds. Accurate recognition of accented speech is a pivotal step toward democratizing AI-driven communication products.