使用Xcelium Machine Learning技术加速验证覆盖率收敛

Accelerating verification coverage convergence using Xcelium Machine Learning technology

随着设计越来越复杂,受约束的随机化验证方法已成为验证的主流方法。一般地,验证激励做到不违反spec描述条件下尽量随机,这样验证能跑到的空间才更充分。但是,这给功能覆盖率收敛带来极大挑战,为解决这一难题,Cadence率先推出了仿真器的机器学习功能——Xcelium Machine Learning,采用机器学习技术让功能覆盖率快速收敛,大大提高验证仿真效率。介绍了Xcelium Machine Learning的使用流程,并给出在相同模拟(simulation)验证环境下应用Machine Learning前后情况对比。最后Machine Learning在模拟(simulation)验证中的应用前景进行了展望。

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.