Accelerating Coverage Directed Test Generation for Functional Verification: A Neural Network-based Framework

Fanchao Wang, Hanbin Zhu, Pranjay Popli, Yao Xiao, P. Bogdan, Shahin Nazarian
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引用次数: 20

Abstract

With increasing design complexity, the correlation between test transactions and functional properties becomes non-intuitive, hence impacting the reliability of test generation. This paper presents a modified coverage directed test generation based on an Artificial Neural Network (ANN). The ANN extracts features of test transactions and only those which are learned to be critical, will be sent to the design under verification. Furthermore, the priority of coverage groups is dynamically learned based on the previous test iterations. With ANN-based screening, low-coverage or redundant assertions will be filtered out, which helps accelerate the verification process. This allows our framework to learn from the results of the previous vectors and use that knowledge to select the following test vectors. Our experimental results confirm that our learning-based framework can improve the speed of existing function verification techniques by 24.5x and also also deliver assertion coverage improvement, ranging from 4.3x to 28.9x, compared to traditional coverage directed test generation, implemented in UVM.
加速功能验证的覆盖导向测试生成:一个基于神经网络的框架
随着设计复杂性的增加,测试事务和功能属性之间的相关性变得不直观,从而影响测试生成的可靠性。提出了一种改进的基于人工神经网络的覆盖定向测试生成方法。人工神经网络提取测试事务的特征,只有那些学习到是关键的,才会被发送到正在验证的设计中。此外,覆盖组的优先级是基于之前的测试迭代动态学习的。通过基于人工神经网络的筛选,低覆盖率或冗余的断言将被过滤掉,这有助于加快验证过程。这允许我们的框架从之前的向量的结果中学习,并使用这些知识来选择下面的测试向量。我们的实验结果证实,与在UVM中实现的传统覆盖导向测试生成相比,我们基于学习的框架可以将现有功能验证技术的速度提高24.5倍,并且还可以提供断言覆盖率改进,范围从4.3倍到28.9倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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