中圖分類號(hào)：TN402 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.239805
中文引用格式： 植玉，馬業(yè)欣，徐嶸. 使用Xcelium Machine Learning技術(shù)加速驗(yàn)證覆蓋率收斂[J]. 電子技術(shù)應(yīng)用，2023，49(8)：19-23.
英文引用格式： Zhi Yu，Ma Yexin，Xu Rong. Accelerating verification coverage convergence using Xcelium Machine Learning technology[J]. Application of Electronic Technique，2023，49(8)：19-23.

Accelerating verification coverage convergence using Xcelium Machine Learning technology

Abstract： As designs become more complex, constrained randomized verification methods have become the mainstream method for verification. Generally, the verification incentive should be as random as possible without violating the spec description condition, so that the space that the verification can cover is more sufficient. However, this brings great challenges to the convergence of functional coverage. To solve this problem, Cadence pioneered the machine learning function of the simulator - Xcelium Machine Learning, which uses machine learning technology to quickly converge the functional coverage and greatly improve the efficiency of verification simulation. This article mainly introduces the process of using Xcelium Machine Learning and gives a comparison before and after using machine learning in the same simulation verification environment. Finally, the application prospect of machine learning in simulation verification is prospected.

Key words : random test；constrained random；functional coverage；machine learning；simulation