LLM4HW
Large Language Models for Hardware
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Papers from the LLM4HW team:
Explaining EDA synthesis errors with LLMs
ISLAD'24
Training new engineers in digital design is a challenge, particularly when it comes to teaching the complex electronic design automation (EDA) tooling used in this domain. Learners will typically deploy designs in the Verilog and VHDL hardware description languages to Field Programmable Gate Arrays (FPGAs) from Altera (Intel) and Xilinx (AMD) via proprietary closed-source toolchains (Quartus Prime and Vivado, respectively)....
Read more → 2024Evaluating LLMs for Hardware Design and Test
ISLAD'24
Large Language Models (LLMs) have demonstrated capabilities for producing code in Hardware Description Languages (HDLs). However, most of the focus remains on their abilities to write functional code, not test code. The hardware design process consists of both design and test....
Read more → 2024Verigen: A large language model for verilog code generation
ACM
In this study, we explore the capability of Large Language Models (LLMs) to automate hardware design by automatically completing partial Verilog code, a common language for designing and modeling digital systems. We fine-tune pre-existing LLMs on Verilog datasets compiled from GitHub and Verilog textbooks....
Read more → 2024On hardware security bug code fixes by prompting large language models
IEEE Transactions on Information Forensics and Security
Novel AI-based code-writing Large Language Models (LLMs) such as OpenAI’s Codex have demonstrated capabilities in many coding-adjacent domains. In this work, we consider how LLMs may be leveraged to automatically repair identified security-relevant bugs present in hardware designs by generating replacement code...
Read more → 2024Chip-Chat: Challenges and Opportunities in Conversational Hardware Design
MLCAD'23
Modern hardware design starts with specifications provided in natural language. These are then translated by hardware engineers into appropriate Hardware Description Languages (HDLs) such as Verilog before synthesizing circuit elements. Automating this translation could reduce sources of human error from the engineering process. But, it is only recently that artificial intelligence (AI) has demonstrated capabilities for machine-based end-to-end design translations.....
Read more → 2023