Machine Learning-based Structural Pre-route Insertability Prediction and Improvement with Guided Backpropagation
Autor: | Kai-Shun Hu, Chin-Hsiung Hsu, Shao-Yun Fang, Hsien-Shih Chiu, Philip Hui-Yuh Tai, Tao-Chun Yu, Cindy Chin-Fang Shen |
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Rok vydání: | 2021 |
Předmět: |
010302 applied physics
Standard cell Computer science business.industry 02 engineering and technology Integrated circuit Machine learning computer.software_genre 01 natural sciences Electromigration Backpropagation 020202 computer hardware & architecture law.invention Power (physics) Visualization law 0103 physical sciences Hardware_INTEGRATEDCIRCUITS 0202 electrical engineering electronic engineering information engineering Overhead (computing) Artificial intelligence Low resistance business computer |
Zdroj: | ASP-DAC |
Popis: | With the development of semiconductor technology nodes, the sizes of standard cells become smaller and the number of standard cells is dramatically increased to bring into more functionality in integrated circuits (ICs). However, the shrinking of standard cell sizes causes many problems of ICs such as timing, power, and electromigration (EM). To tackle these problems, a new style structural pre-route (SPR) is proposed. Such type of pre-route is composed of redundant parallel metals and vias so that the low resistance and the redundant sub-structures can improve timing and yield. But the large area overhead becomes the major problem of inserting such pre-routes all over a design. In this paper, we propose a machine learning-based approach to predict the insertability of SPRs for placed designs. In addition, we apply a pattern visualization method by using a guided backpropagation technique to see in depth of our model and identify the problematic layout features causing SPR insertion failures. The experimental results not only show the excellent performance of our model, but also show that avoiding generating the identified critical features during legalization can improve SPR insertability compared to a commercial SPR-aware placement tool. |
Databáze: | OpenAIRE |
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