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Machine Learning Approach for Fast Electromigration Aware Aging Prediction in Incremental Design of Large Scale On-Chip Power Grid Network

Researchers use a machine learning (ML) approach to obtain the EM-aware aging prediction of the power grid (PG) network. They use neural network–based regression as their core ML technique to instantly predict the lifetime of a perturbed PG network.

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Abstract
“With the advancement of technology nodes, Electromigration (EM) signoff has become increasingly difficult, which requires a considerable amount of time for an incremental change in the power grid (PG) network design in a chip. The traditional Black’s empirical equation and Blech’s criterion are still used for EM assessment, which is a time-consuming process. In this article, for the first time, we propose a machine learning (ML) approach to obtain the EM-aware aging prediction of the PG network. We use neural network–based regression as our core ML technique to instantly predict the lifetime of a perturbed PG network. The performance and accuracy of the proposed model using neural network are compared with the well-known standard regression models. We also propose a new failure criterion based on which the EM-aging prediction is done. Potential EM-affected metal segments of the PG network is detected by using a logistic-regression–based classification ML technique. Experiments on different standard PG benchmarks show a significant speedup for our ML model compared to the state-of-the-art models. The predicted value of MTTF for different PG benchmarks using our approach is also better than some of the state-of-the-art MTTF prediction models and comparable to the other accurate models.”

Find the technical paper link here (author site) or here (ACM Journal). Published 2020.

Dey, Sukanta, Sukumar Nandi, and Gaurav Trivedi. “Machine learning approach for fast electromigration aware aging prediction in incremental design of large scale on-chip power grid network.” ACM Transactions on Design Automation of Electronic Systems (TODAES) 25.5 (2020), 1-29. DOI: 10.1145/3399677.

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