https://scholars.lib.ntu.edu.tw/handle/123456789/580896
標題: | Dynamic IR-Drop ECO Optimization by Cell Movement with Current Waveform Staggering and Machine Learning Guidance | 作者: | Huang X.-X Chen H.-C Wang S.-W Jiang I.H.-R Chou Y.-C Tsai C.-H. HUI-RU JIANG |
關鍵字: | Computer aided design; Cytology; Drops; Machine learning; Predictive analytics; Circuit performance; Current practices; Functional failure; Ground networks; Hot-spot cells; Optimization scheme; Prediction model; Timing information; Cells | 公開日期: | 2020 | 卷: | 2020-November | 來源出版物: | IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD | 摘要: | Excessive dynamic IR-drop degrades the circuit performance and may lead to functional failure. Existing IR-drop fixing techniques at the placement stage do not consider the time-variant property and thus cannot handle dynamic IR-drop hotspots well. In current practice, designers perform Engineer Change Order (ECO) to move out these hotspot cells based on their experience. In this paper, we present a novel dynamic IR-drop ECO optimization and prediction framework by wise cell movement. We first spread high demand current cells in a global view to stagger their current waveforms. Then, we further move IR hotspot cells close to power/ground (PG) vias for minimizing the resistance from PG pads to their PG pins. Moreover, we propose an accurate machine learning-based dynamic IR-drop prediction model to guide the final cell movement. The features of our model capture power ground network characteristics, timing information, and cumulative current drawn by cells, thus leading to a general model applicable to ECO. Experimental results show that our proposed model precisely predicts dynamic IR-drop after cell movement, and our optimization scheme can substantially alleviate dynamic IR-drop without timing degradation. ? 2020 Association on Computer Machinery. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097922384&doi=10.1145%2f3400302.3415614&partnerID=40&md5=add5ad931cc9de550baa6bf49eaa2240 https://scholars.lib.ntu.edu.tw/handle/123456789/580896 |
ISSN: | 10923152 | DOI: | 10.1145/3400302.3415614 |
顯示於: | 電機工程學系 |
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