Presentation
OpenAssert: Towards Open-Source Large Language Models for Assertion Generation
DescriptionAssertions are essential for hardware verification but are typically generated manually, leading to long development cycles. While commercial Large Language Models (LLMs) like GPT-4 show promise for automating assertion generation, they raise concerns about IP privacy and data confidentiality. This paper proposes OpenAssert, an approach for generating assertions locally using open-source LLMs. We enhance these models with Retrieval Augmentation Generation (RAG) to reduce errors and hallucinations. OpenAssert improves by up to 44% in rouge-1 score, 49% in cosine similarity, and reduces word error rate by 43.4%, outperforming GPT-4 by 23.7%, with 100% line coverage in our evaluation.
Event Type
Networking
Work-in-Progress Poster
TimeSunday, June 226:00pm - 7:00pm PDT
LocationLevel 3 Lobby