This paper presents an integrated IR-Informed Timing and Timing-Aware IR Optimization flow with an IR-drop predictor. The proposed flow couples an IR-Informed Timing Optimizer with a Timing-Aware IR Optimizer to consider the mutual impact between IR-drop and timing during optimization. Then, we leverage a fast ML-based IR-drop predictor to quickly estimate the IR-drop after each iteration of optimization, which enables fast switching between the IR optimizer and timing optimizer. We further propose Feature Approximation to speed up the inference time of the IR-drop predictor. On two 7nm designs, the proposed flow closes timing and eliminates at least 90.6% of IR-drop violations. The Feature Approximation achieves 67% speed up in the runtime of the overall flow. Our optimization flow can be applied to a 945k-cell design with 7,578 IR-drop violations within 3 hours, demonstrating its practicality.

An energy-efficient high-performance static IR-drop estimation method based on green learning called Green IR Drop (GIRD) is proposed in this work. GIRD processes the IC design input in three steps. First, the input netlist data are converted to multichannel maps. Their joint spatial–spectral representations are determined with PixelHop. Next, discriminant features are selected using the relevant feature test (RFT). Finally, the selected features are fed to the eXtreme Gradient Boosting trees regressor. Both PixelHop and RFT are green learning tools. GIRD yields a low carbon footprint due to its smaller model sizes and lower computational complexity. Besides, its performance scales well with small training datasets. Experiments on synthetic and real circuits are given to demonstrate the superior performance of GIRD. The model size and the complexity, measured by the floating point operations (FLOPs) of GIRD, are only$10^{-3}$and$10^{-2}$of deep-learning methods, respectively.

IR drop estimation is now considered a first-order metric due to the concern about reliability and performance in modern electronic products. Since traditional solution involves lengthy iteration and simulation flow, how to achieve fast yet accurate estimation has become an essential demand. In this work, with the help of modern AI acceleration techniques, we propose a comprehensive solution to combine both the advantages of image-based and netlist-based features in neural network framework and obtain high-quality IR drop prediction very effectively in modern designs. A customized convolutional neural network (CNN) is developed to extract PDN features and make static IR drop estimations. Trained and evaluated with the open-source dataset, experiment results show that we have obtained the best quality in the benchmark on the problem of IR drop estimation in ICCAD CAD Contest 2023, proving the effectiveness of this important design topic.

In recent disasters, the web has served as a medium of communication among disaster response teams, survivors, local citizens, curious onlookers, and zealous people who are willing to assist victims affected by disasters. To encourage and speed up information dissemination, the availability and convenience of use are normally the top concerns in designing disaster response web services, where a design of free-formed inputs without access control is commonly adopted. However, such design may result in personal information disclosure and privacy leakage. In this paper, using a case study of a real-life disaster response service, the MKER (Morakot Event Reporting) forum, we show that the disclosure of personal information and the resulting privacy disclosure is indeed a serious problem that is currently happening. In our case, we have successfully mapped 1,438 unique cell phone numbers and 1,383 unique addresses to individuals using an automated method, not to mention the much greater invasion of privacy that could be effected by manual analysis of the messages posted on the forum. To resolve this issue, we propose several means to mitigate and prevent the mentioned privacy leakage on disaster response services from being happened.