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Machine Learning in the Service of Hardware Functional Verification

In: Machine Learning Applications in Electronic Design Automation

Author

Listed:
  • Raviv Gal

    (IBM Research)

  • Avi Ziv

    (IBM Research)

Abstract

Modern hardware functional verification employs many different tools alongside large compute farms to ensure that the hardware’s implementation matches its specification. This produces a lot of data that can be used to better monitor and control the verification process. Data science in general and machine learning specifically are disciplines in computer science that deal with extracting patterns and information from datasets. This chapter shows how these technologies can be integrated into the verification process in a holistic manner and become an integral part of the verification process backbone. The chapter begins with examples on the use of machines learning in specific verification tools. This is followed by a description of how to connect the various verification tools and data sources and create a unified data repository in a data warehouse that is optimized for data retrieval. This connection allows the use of advanced data science techniques that improve the quality of the entire verification process.

Suggested Citation

  • Raviv Gal & Avi Ziv, 2022. "Machine Learning in the Service of Hardware Functional Verification," Springer Books, in: Haoxing Ren & Jiang Hu (ed.), Machine Learning Applications in Electronic Design Automation, chapter 0, pages 377-424, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-13074-8_14
    DOI: 10.1007/978-3-031-13074-8_14
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