High-speed link verification based on statistical inference
Xuan Zeng, Chenlei Fang, Qicheng Huang, Fan Yang, Dian Zhou, Wei Cai, Weiping Shi · 2016
High-speed I/O link plays an important role in modern computer systems. In order to accurately estimate a small BER value in the order of 10-12, a large number of bits need to be transmitted, which results in expensive testing cost. In this paper, we exploit the correlation between the performance of high-speed I/O link under different corners/configurations to improve the accuracy of the estimated BER. A graphical generative model is used to represent the underlying correlations. This template provides a way to share information between different models, hence increases the modeling accuracy. Experimental results show that our method achieves up to 2x speed-up over the traditional method.