可扩展的跟踪信号选择使用机器学习

Kamran Rahmani, P. Mishra, S. Ray
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引用次数: 21

摘要

后硅验证中的一个关键问题是识别一小组可跟踪信号,这些信号在硅执行期间对调试有效。传统的信号选择技术采用结构分析,导致恢复质量较差。相比之下,基于模拟的选择技术提供了优越的可恢复性,但会产生显著的计算开销。在本文中,我们提出了一种有效的信号选择技术,使用机器学习来利用基于仿真的信号选择,同时显着降低仿真开销。我们的方法使用(1)有界模拟模拟为机器学习技术生成训练向量集,以及(2)一种消除方法来识别最有利可图的信号集。实验结果表明,我们的方法可以在更快或相当的运行时间内提高高达63.3%(平均17.2%)的可恢复性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Scalable trace signal selection using machine learning
A key problem in post-silicon validation is to identify a small set of traceable signals that are effective for debug during silicon execution. Structural analysis used by traditional signal selection techniques leads to poor restoration quality. In contrast, simulation-based selection techniques provide superior restorability but incur significant computation overhead. In this paper, we propose an efficient signal selection technique using machine learning to take advantage of simulation-based signal selection while significantly reducing the simulation overhead. Our approach uses (1) bounded mock simulations to generate training vectors set for the machine learning technique, and (2) an elimination approach to identify the most profitable signals set. Experimental results indicate that our approach can improve restorability by up to 63.3% (17.2% on average) with a faster or comparable runtime.
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