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研究生: 鄧有倫
Deng, You-Lun
論文名稱: 基於模擬演化演算法以可繞度為導向並能遵循設計階層之巨集電路擺置器
A Routability-Driven Macro Placer Following Design Hierarchy Based on Simulated Evolution Algorithm
指導教授: 林家民
Lin, Jai-Ming
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 電機工程學系
Department of Electrical Engineering
論文出版年: 2018
畢業學年度: 106
語文別: 英文
論文頁數: 37
中文關鍵詞: 實體設計階層式設計模組擺置可繞度
外文關鍵詞: physical design, design hierarchy, macro placement, routability
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  • 隨著製程的進步,現今電路設計變得越來越複雜,往往晶片中包含了數以百計的模組。由於模組的擺置會影響到標準邏輯閘的散佈,以及模組附近容易會有窄通道或者是模組過大而造成標準邏輯閘額外的繞線,這些問題對於總體繞線長度以及可繞度都有很大的影響。而現今的商業工具還未能在模組擺置階段得到一個可接受的擺置結果,模組擺置仍仰賴工程師的優化。除此之外,電路中往往存有預先擺置模組,預先擺置的模組容易讓模組擺置的區域更加不平整,導致模組擺置的複雜度更大幅的提升。
    本篇論文採用三階段混合尺寸擺置的流程,包括:(1)初始擺置 (2)模組擺置 (3)標準邏輯閘擺置。其中我們專注在模組擺置的研究。首先,提出了擺置障礙物與預先擺置模組合併,它能有效的減少障礙物的數量;接著根據模組的階層式名稱建立階層樹,透過階層樹去群集階層相近的模組,有利於將功能相同的子電路擺置在鄰近區域;利用遞迴切割的方法,將過度密集區域的模組群集平均分配,能有效避免區域壅擠;接著利用模組合法化擺置器將子區域內的模組進行合法化,然後進入模組優化階段,採用模擬演化演算法更進一步的優化模組擺置,最後將模組擺置結果輸入到ICC (IC-Compiler) [32] 進行最後的標準邏輯閘擺置。實驗結果證實,我們的演算法能有效增加電路的可繞度,相較於前人的方法 [25] 能夠更有效率的合法化模組擺置。

    Due to advance in manufacture technology, circuit designs become more and more complicated, and usually contain hundreds of macros. Macro placement is one of the most important stages since its result has great impact on wirelength and routability. Either the locations of macros will influence the distribution of standard cells or it is much easier to have routing congestions around macros. Furthermore, macro placement problems become more difficult because of preplaced macros. However, commercial tools cannot obtain acceptable results for macro placement, which still rely on experienced engineers to refine locations of macros. Therefore, this thesis adopts three-stage mixed size placement flow, which consists of: (1) placement prototyping, (2) macro placement, (3) standard-cell placement. Our research focuses on macro placement. In the beginning, we propose a placement blockage merger that can reduce the number of obstacles. Then, we group macros according to their hierarchies and the expansion of area of macros. Next, we redistribute macro groups by a recursive partition, where we can redistribute macro groups uniformly. Finally, we propose a macro legalizer based on corner stitching [26] to legalize macros in subregions, and refine macro legalization based on simulated evolution algorithm. We compare our method with the greedy algorithm [25]. Experimental results demonstrate our method can legalize macros efficiently, and can be better than the previous work.

    摘要 I Abstract II 誌謝 III Table of Contents IV List of Tables VI List of Figures VII Chapter 1 Introduction 1 1.1 Previous Works 3 1.1.1 B*-tree Based Algorithms 3 1.1.2 Circular-Contour Based Algorithms 4 1.2 Inaccurate Evaluation of Macro Placement Quality 4 1.3 Design Hierarchy 6 1.4 Our Contributions 7 1.5 Thesis Organization 8 Chapter 2 Preliminaries 9 2.1 Corner Stitching Data Structure 9 2.1.1 Point Finding 10 2.1.2 Neighbor Finding 11 2.1.3 Directed Area Enumeration 11 2.2 Problem Formulation 12 Chapter 3 Macro Legalization Methodology 14 3.1 Overview of Our Proposed Methodology 14 3.2 Preprocessing 15 3.3 Macro Grouping 17 3.3.1 Construction of A Hierarchy Tree 17 3.3.2 Macro Grouping Algorithm 19 3.4 Marco Group Redistribution 20 3.5 Macro Legalization 23 3.5.1 Macro Legalization Based on Corner Stitching 23 3.5.2 Cost Evaluation 25 3.6 Macro Refinement 26 3.6.1 Score Function 26 3.6.2 Macro Refinement Algorithm 27 Chapter 4 Experimental Results 29 4.1 Experimental Environment 29 4.2 Experimental Results of Our Macro Legalization Algorithm 30 Chapter 5 Conclusion 33 Bibliography 34

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