LLM4DV: Using Large Language Models for Hardware Test Stimuli Generation
Zixi Zhang Greg Chadwick Hugo McNally Yiren Zhao Robert Mullins
Test stimuli generation has been a crucial but labor-intensive task in
hardware design verification. In this paper, we revolutionize this process by
harnessing the power of large language models (LLMs) and present a novel
benchmarking framework, LLM4DV. This framework introduces a prompt template for
interactively eliciting test stimuli from the LLM, along with four innovative
prompting improvements to support the pipeline execution and further enhance
its performance. We compare LLM4DV to traditional constrained-random testing
(CRT), using three self-designed design-under-test (DUT) modules. Experiments
demonstrate that LLM4DV excels in efficiently handling straightforward DUT
scenarios, leveraging its ability to employ basic mathematical reasoning and
pre-trained knowledge. While it exhibits reduced efficiency in complex task
settings, it still outperforms CRT in relative terms. The proposed framework
and the DUT modules used in our experiments will be open-sourced upon
publication.