使用機器學習之自動化電路壓降工程修改命令

Automatic IR-Drop ECO Using Machine Learning

林恆毅(Heng-Yi Lin)

指導教授 ： 李建模

國立臺灣大學/電機資訊學院/電子工程學研究所/碩士(2018年)

https://doi.org/10.6342/NTU201803107

摘要

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。

關鍵字

電路壓降 ； 工程修改命令 ； 機器學習

並列摘要

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.

並列關鍵字

IR-drop ； ECO ； machine learning

參考文獻

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