Presentation
Intelligence In The Fence: Construct A Privacy and Reliable Hardware Design Assistant LLM
DescriptionLarge language models (LLMs) have been widely used in software and hardware areas to help developers generate high-quality code quickly. While existing solutions often rely on commercial LLMs, such as the popular ChatGPT, the trend of training a local model for code generation is growing due to the security concerns of releasing proprietary data to third-party service providers. Still, in the hardware domain, due to the lack of high-quality training datasets, researchers have to rely on commercial LLMs, facing the issue of private training data leakage. This paper adheres to the principle of zero data upload to address data privacy concerns. Instead of commercial LLMs, we propose a localized and transparent solution leveraging local LLMs to synthesize data and eliminate data leakage risks. To overcome offline LLMs' low-performance issues, we propose an innovative approach to constructing code descriptions based on code interpretation. This approach addresses the challenge that even third-party high-performance LLMs, despite their capabilities, still require manually crafted prompts and cannot ensure the generation of high-quality hardware designs. The proposed training process and the new dataset structure help us locally train a hardware design assistant LLM named PrivacyGen. The generated PrivacyGen performs similarly to GPT-4 in complex hardware design generation but has a much smaller size and low total cost of ownership.
Event Type
Networking
Work-in-Progress Poster
TimeSunday, June 226:00pm - 7:00pm PDT
LocationLevel 3 Lobby