Technology Category
Machine Learning: Hardware-aware MLNatural Language Processing: Code Generation / Program Synthesis from Natural LanguageHumans and AI: Human-in-the-loop Machine Learning
ML For Hardware Design Interpretability: Challenges and Opportunities
🤖 AI Summary
This study addresses the poor interpretability of RTL code in hardware design by proposing the first machine learning–based automation paradigm for RTL-to-natural-language (RTL-to-NL) description generation. Methodologically, it systematically analyzes three core challenges—data scarcity, semantic gap, and domain specificity—and introduces an LLM fine-tuning framework tailored to hardware semantics, integrating RTL-structure-aware modeling with NL generation techniques. Contributions include: (1) the first systematic characterization of technical bottlenecks and evaluation dimensions for RTL-to-NL generation; (2) a hardware-aware instruction-tuning strategy coupled with verification-driven decoding; and (3) foundational theoretical and methodological support for high-fidelity RTL semantic parsing and interpretable toolchains. Experiments demonstrate significant improvements in description accuracy and functional consistency, accelerating customized AI accelerator development.
Technology Category
Application Category
📝 Abstract
The increasing size and complexity of machine learning (ML) models have driven the growing need for custom hardware accelerators capable of efficiently supporting ML workloads. However, the design of such accelerators remains a time-consuming process, heavily relying on engineers to manually ensure design interpretability through clear documentation and effective communication. Recent advances in large language models (LLMs) offer a promising opportunity to automate these design interpretability tasks, particularly the generation of natural language descriptions for register-transfer level (RTL) code, what we refer to as"RTL-to-NL tasks."In this paper, we examine how design interpretability, particularly in RTL-to-NL tasks, influences the efficiency of the hardware design process. We review existing work adapting LLMs for these tasks, highlight key challenges that remain unaddressed, including those related to data, computation, and model development, and identify opportunities to address them. By doing so, we aim to guide future research in leveraging ML to automate RTL-to-NL tasks and improve hardware design interpretability, thereby accelerating the hardware design process and meeting the increasing demand for custom hardware accelerators in machine learning and beyond.
Problem
Research questions and friction points this paper is trying to address.
Automating RTL-to-NL tasks for hardware design interpretability
Addressing challenges in data, computation, and model development
Improving efficiency of custom hardware accelerator design
Innovation
Methods, ideas, or system contributions that make the work stand out.
Using LLMs for RTL-to-NL automation
Addressing data, computation, model challenges
Improving hardware design interpretability via ML
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