论文标题
MAVIREC：ML辅助的矢量ir-避免和分类
MAVIREC: ML-Aided Vectored IR-DropEstimation and Classification
论文作者
论文摘要
Vectored IR Drop Analysis是CHIP签名的关键步骤，该步骤可以检查芯片电源输送网络的功率完整性。由于动态IR滴分析的过度运行时间，必须将大量的测试模式降低到一小部分最差的IR载体。与传统的慢速启发式方法选择了一些具有不完整覆盖的向量的传统慢速启发式方法不同，Mavirec使用机器学习技术 - 3D卷积和类似回归的层 - 准确地推荐了更大的测试模式的子集，以锻炼最坏情况。在不到30分钟的时间内，Mavirec概要文件100K周期矢量，并且比最先进的工业流提供了更好的覆盖范围。此外，Mavirec的IR Drop预测变量显示，相对于工业流量，其4MV RMSE的速度为10倍。
Vectored IR drop analysis is a critical step in chip signoff that checks the power integrity of an on-chip power delivery network. Due to the prohibitive runtimes of dynamic IR drop analysis, the large number of test patterns must be whittled down to a small subset of worst-case IR vectors. Unlike the traditional slow heuristic method that select a few vectors with incomplete coverage, MAVIREC uses machine learning techniques -- 3D convolutions and regression-like layers -- for accurately recommending a larger subset of test patterns that exercise worst-case scenarios. In under 30 minutes, MAVIREC profiles 100K-cycle vectors and provides better coverage than a state-of-the-art industrial flow. Further, MAVIREC's IR drop predictor shows 10x speedup with under 4mV RMSE relative to an industrial flow.
