LLM-Based Processor Verification: A Case Study for Neuronnorphic Processor

Chao Xiao, Yifei Deng, Zhijie Yang, Renzhi Chen, Hong Wang, Jingyue Zhao, Huadong Dai, Lei Wang, Yuhua Tang, Weixia Xu

Abstract: With the increasing complexity of the hardware design, conducting verification before the tapeout is of utmost importance. Simulation-based verification remains the primary method owing to its scalability and flexibility. A comprehensive verification of modern processors usually requires numerous effective tests to cover all possible conditions and use cases, leading to significant time, resource, and manual effort even with the EDA. Moreover, novel domain specific architecture (DSA), such as neuromorphic processors, will exacerbate the challenge of verification. Fortunately, emerging large language models (LLMs) have been demonstrating a powerful ability to complete specific tasks assigned by human instructions. In this paper, we explore the challenges and opportunities encountered when using the LLMs to accelerate the DSA verification using the proposed LLM-based workflow consisting of test generation, compilation&simulation, and result collection&processing. By verifying a RISC-V core and a neuromorphic processor, we examine the capabilities and limitations of the LLMs when using them for the function verification of traditional processors and emerging DSA. In the experiment, 36 $C$ programs and 128 assembly snippets for the RISC-V core and the neuromorphic processor are generated using an advanced LLM to demonstrate our claim. The experimental results show that the code coverage based on the LLM test generation can reach 89% and 91% for the above two architectures respectively, showing a promising research direction for the future processor verification in the new golden age for computer architecture.

External IDs:dblp:conf/date/XiaoDYCWZDWTX24