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The traditional Black\u2019s empirical equation and Blech\u2019s 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.<\/jats:p>","DOI":"10.1145\/3399677","type":"journal-article","created":{"date-parts":[[2020,7,6]],"date-time":"2020-07-06T04:05:42Z","timestamp":1594008342000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Machine Learning Approach for Fast Electromigration Aware Aging Prediction in Incremental Design of Large Scale On-chip Power Grid Network"],"prefix":"10.1145","volume":"25","author":[{"given":"Sukanta","family":"Dey","sequence":"first","affiliation":[{"name":"Indian Institute of Technology Guwahati, Guwahati, Assam, India"}]},{"given":"Sukumar","family":"Nandi","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Guwahati, Guwahati, Assam, India"}]},{"given":"Gaurav","family":"Trivedi","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Guwahati, Guwahati, Assam, India"}]}],"member":"320","published-online":{"date-parts":[[2020,7,5]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Synopsys Inc. 2019. 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