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Machine learning based effective linear regression model for TSV layer assignment in 3DIC

机译:基于机器学习的3DIC TSV层分配有效线性回归模型

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摘要

On the integration of 3D IC design, thermal management issues play a significant role. So, it is required to implement an effective approaches and solutions for integrating 3DIC. The TSV causes problems with the distinct coefficients of thermal expansion that induces mismatch strains and stresses. The major drawback of 3DIC is the thermal management issues which increases the power consumption through the current crowding, perhaps the temperature upraised by the slacked layers due to its heat generation. Several research has not been undergone in 3DIC utilizing machine learning approaches which is highly complicated. This paper firstly proposes an efficient ML model to achieve better reduction in wire length and temperature. An efficient linear regression model is preferred here in order to achieve significant performances in TSV layer assignment. The linear regression utilized gradient based approach where the error is predicted at every instance through tracing gradient cost function. An optimized TSV layer assignment is achieved with this flexible ELRM. The performance analysis of data shows that the proposed ELRM based TSV assignment achieved better wire length and temperature. The ISPD98 Circuit Benchmark Suite is utilized for result evaluation and it achieves improved TSV layer assignment through reducing wire length and temperature.
机译:在3D IC设计的集成上,热管理问题发挥了重要作用。因此,需要为整合3DIC实施有效的方法和解决方案。 TSV导致诱导不匹配菌株和应力的不同热膨胀系数的问题。 3DIC的主要缺点是通过当前拥挤增加功耗的热管理问题,可能由于其发热而被储存层的温度升高。 3DIC利用机器学习方法尚未经历过几项研究,这是非常复杂的。本文首先提出了一种高效的ML模型,以实现线材长度和温度的更好降低。这里优选一种有效的线性回归模型,以便在TSV层分配中实现显着性能。线性回归利用基于梯度的方法,其中通过跟踪梯度成本函数在每个实例中预测错误。通过该柔性ELRM实现优化的TSV层分配。数据的性能分析表明,所提出的基于ELRM的TSV任务实现了更好的线材长度和温度。 ISPD98电路基准套件用于结果评估,通过降低线材长度和温度,实现了改进的TSV层分配。

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