Adapting AI into Low Power Testing

Hillol Maity, S. Chattopadhyay
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Abstract

11This work is partially supported by the research project sponsored by the Synopsys Inc., USAAdvancements in data science have enabled various fields to achieve unparalleled performance and efficiency enhancement. Different domains of Artificial Intelligence (AI) held the center of interest for researchers, academic and industrial practitioners throughout the last decade. When it comes to the low power testing, it still has a wide range of possibilities where existing methods and techniques can be made more efficient as well as faster with the integration of AI. In this proposal, first, we have come up with a new test vector reordering technique that attempts to reduce both shift and capture power during testing. Secondly, we claim that the use of AI models can significantly speed up such techniques where repeated simulation-based value estimation remains an essential bottleneck. We verify our claim by engaging a deep neural network (DNN) based predictive model to replace the repetitive simulation-based method on this new reordering technique. Experimental results show that the AI-based framework can speed up the simulation-based framework by 162 times, on average.
将AI应用于低功耗测试
这项工作得到了美国新思公司(Synopsys Inc.)赞助的研究项目的部分支持。数据科学的进步使各个领域实现了无与伦比的性能和效率提升。在过去的十年里,人工智能(AI)的不同领域一直是研究人员、学术和工业从业者的兴趣中心。当涉及到低功耗测试时,它仍然有广泛的可能性,现有的方法和技术可以通过人工智能的集成而变得更高效和更快。在本提案中,首先,我们提出了一种新的测试向量重新排序技术,该技术试图减少测试期间的移位和捕获功率。其次,我们声称人工智能模型的使用可以显著加快这些基于重复模拟的价值估计仍然是一个重要瓶颈的技术。我们通过使用基于深度神经网络(DNN)的预测模型来取代基于重复模拟的方法来验证我们的说法。实验结果表明,基于人工智能的框架可以使基于仿真的框架平均提速162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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