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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李建模 | |
| dc.contributor.author | Heng-Yi Lin | en |
| dc.contributor.author | 林恆毅 | zh_TW |
| dc.date.accessioned | 2021-06-17T04:28:49Z | - |
| dc.date.available | 2021-09-01 | |
| dc.date.copyright | 2018-08-18 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-08-13 | |
| dc.identifier.citation | [Bhamidipati 2017] Bhamidipati, Lakshmi, et al. “A Power Delivery Network and Cell Placement Aware IR-drop Mitigation Technique: Harvesting Unused Timing Slacks to Schedule Useful Skews,” VLSI (ISVLSI), 2017 IEEE Computer Society Annual Symposium on. IEEE, 2017.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/70467 | - |
| dc.description.abstract | 本論文提出一種通過工程修改命令(Engineering Change Order or ECO)修復電路壓降違規的自動化流程。 我們的工程修改命令技術提供元件(cell)移動(move)和縮小尺寸(downsize)的解決方案。 我們使用機器學習來預測電路壓降，以便我們可以防止過度修理。 我們使用商業軟體來預測時序，所以這是一個時序感知的工程修改命令。 通過以上兩個預測，我們提出了一種新穎的多輪二分匹配，以優化工程修改命令之資源利用率。 實驗結果顯示，對於500萬元件的實際設計，我們提出的方法修復了原始11,555個違規元件(violation cell)中的2,504(22％)個違規元件，並修復了原始98,674 mV總過量電路壓降中的36,272 mV(37％)總過量電路壓降。 我們能夠在13個小時內對7千個元件進行工程修改命令，因此我們的工程修改命令流程非常實用，可以應用於大型工業設計。 | zh_TW |
| dc.description.abstract | This thesis proposes an automatic flow to repair IR-drop violations by Engineering Change Order (ECO). Our ECO technique provides cell move and downsize solutions. We use machine learning to predict IR-drop so that we can prevent over-fixing. We use a commercial tool to predict timing so that this is a timing-aware ECO. With the above two predictions, we propose a novel multi-round bipartite matching to optimize the ECO resource utilization. Experimental results show that for a 5M gate real design, our proposed method repairs 2,504 (22%) violation cells out of the original 11,555 violation cells and repairs 36,272 mV (37%) total excessive IR out of the original 98,674 mV total excessive IR. We are able to perform ECO on seven thousand cells within 13 hours, so our ECO flow is practical and can be applied to large industrial designs. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T04:28:49Z (GMT). No. of bitstreams: 1 ntu-107-R05943084-1.pdf: 1690401 bytes, checksum: 4a2c5b9a4f6e036813d2799c764bc25d (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi LIST OF TABLES vii Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Proposed technique 2 1.3 Contribution 3 1.4 Our assumptions 4 1.5 Organization 4 Chapter 2 Background 5 2.1 Prior work of IR-drop ECO 5 2.2 Machine learning IR-drop prediction 9 2.3 IR-drop analysis 10 Chapter 3 Proposed Techniques 11 3.1 Flow 11 3.2 Define ECO region, IR hotspot and IR coldspot windows 12 3.3 Predict IR-drop contribution 16 3.4 Pre-move 17 3.5 Multi-round bipartite matching 19 3.6 Downsize 25 Chapter 4 Experimental Results 26 4.1 Experimental setup 26 4.2 IR-drop results 30 4.3 Timing results 34 4.4 Run time 35 Chapter 5 Discussion 36 Chapter 6 Conclusion 38 References 39 Appendix 42 | |
| dc.language.iso | en | |
| dc.subject | 電路壓降 | zh_TW |
| dc.subject | 工程修改命令 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | IR-drop | en |
| dc.subject | ECO | en |
| dc.subject | machine learning | en |
| dc.title | 使用機器學習之自動化電路壓降工程修改命令 | zh_TW |
| dc.title | Automatic IR-Drop ECO Using Machine Learning | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳中平,方家偉 | |
| dc.subject.keyword | 電路壓降,工程修改命令,機器學習, | zh_TW |
| dc.subject.keyword | IR-drop,ECO,machine learning, | en |
| dc.relation.page | 43 | |
| dc.identifier.doi | 10.6342/NTU201803107 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2018-08-13 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電子工程學研究所 | zh_TW |
| 顯示於系所單位： | 電子工程學研究所 | |
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