TY - GEN
T1 - ChIRAAG
T2 - 2024 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2024
AU - Mali, Bhabesh
AU - Maddala, Karthik
AU - Gupta, Vatsal
AU - Reddy, Sweeya
AU - Karfa, Chandan
AU - Karri, Ramesh
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - System Verilog Assertion (SVA) formulation- a critical yet complex task is a prerequisite in the Assertion Based Verification (ABV) process. Traditionally, SVA formulation involves expert-driven interpretation of specifications, which is time-consuming and prone to human error. Recently, LLM-informed automatic assertion generation is gaining interest. We designed a novel framework called ChIRAAG, based on OpenAI GPT4, to generate SVA from natural language specifications of a design. ChIRAAG constitutes the systematic breakdown of design specifications into a standardized format, further generating assertions from formatted specifications using LLM. Furthermore, we used few test cases to validate the LLM-generated assertions. Automatic feedback of log messages from the simulation tool to the LLM ensures that the framework can generate correct SVAs. In our experiments, only 27% of LLM-generated raw assertions had errors, which was rectified in few iterations based on the simulation log. Our results on OpenTitan designs show that LLMs can streamline and assist engineers in the assertion generation process, reshaping verification workflows.
AB - System Verilog Assertion (SVA) formulation- a critical yet complex task is a prerequisite in the Assertion Based Verification (ABV) process. Traditionally, SVA formulation involves expert-driven interpretation of specifications, which is time-consuming and prone to human error. Recently, LLM-informed automatic assertion generation is gaining interest. We designed a novel framework called ChIRAAG, based on OpenAI GPT4, to generate SVA from natural language specifications of a design. ChIRAAG constitutes the systematic breakdown of design specifications into a standardized format, further generating assertions from formatted specifications using LLM. Furthermore, we used few test cases to validate the LLM-generated assertions. Automatic feedback of log messages from the simulation tool to the LLM ensures that the framework can generate correct SVAs. In our experiments, only 27% of LLM-generated raw assertions had errors, which was rectified in few iterations based on the simulation log. Our results on OpenTitan designs show that LLMs can streamline and assist engineers in the assertion generation process, reshaping verification workflows.
KW - Assertion Based Verification
KW - Assertion Generation
KW - LLM
UR - http://www.scopus.com/inward/record.url?scp=85206164701&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85206164701&partnerID=8YFLogxK
U2 - 10.1109/ISVLSI61997.2024.00130
DO - 10.1109/ISVLSI61997.2024.00130
M3 - Conference contribution
AN - SCOPUS:85206164701
T3 - Proceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI
SP - 680
EP - 683
BT - 2024 IEEE Computer Society Annual Symposium on VLSI
A2 - Thapliyal, Himanshu
A2 - Becker, Jurgen
PB - IEEE Computer Society
Y2 - 1 July 2024 through 3 July 2024
ER -