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研究生: 龔揚泰
Kung, Yang-Tai
論文名稱: 以可繞度為導向藉由拓樸優化來改善電源網路之方法
Routability-Aware Power Network Refinement Methodology through Topology Optimization
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: 2021
畢業學年度: 109
語文別: 英文
論文頁數: 40
中文關鍵詞: 電源網路最佳化 、電壓降 、可繞度 、繞線擁擠度 、史坦納樹 、單調繞線演算法
外文關鍵詞: IR-drop, Power Delivery Network Optimization, Routability, Routing Congestion, Rectilinear Steiner Minimal Tree, Monotonic Routing Algorithm
相關次數: 點閱:203  下載:1 
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  • 隨著半導體的特徵尺寸持續地縮小以及目前晶片的設計複雜度明顯地上升,在現今晶片的電源網路規劃時面臨到越來越多的挑戰,並且需要在元件擺置階段結束後進行電源網路最佳化。大多數現有的電源網路最佳化方法都致力於如何通過調整電源線段的尺寸來滿足電壓降限制。為了建構一個以可繞度為導向的電源網路,本篇論文開發一種以多種手段去高效且高效率最佳化電源網路的方法,包含電源線段形變階段、電源線段插入階段以及調整電源線段尺寸階段。首先,為了增加電源網路擺置的靈活性,在形變階段會將電源線段分成幾小段並調整它們的位置以減少局部區域的繞線擁擠度。接下來,我們在插入階段中應用兩種不同方法去插入額外的電源線段以最小化電壓降效應。第一個是局部性插入方法,在方法中會根據每個區域對電壓降的影響將電源線段插入到該區域中。另一個則是全域性插入方法,在方法中會藉由違反電壓降限制的點去建構最小直線距離史坦納樹,並且利用單調繞線演算法去決定插入的電源線段的位置。和初始的電源網路相較之下,我們提出的方法不僅可以有效低消除電壓降違規同時對繞線擁擠度的影響也比較小。實驗結果顯示,我們的方法可以工業設計中獲得十分不錯的結果。

    As the feature size of semiconductor shrinks and the complexity of modern design increases significantly, power delivery network (PDN for short) optimization faces more and more challenges. In addition, classic PDN optimizing methods only focus on how to meet the constraints by resizing size of power stripes. This paper develops an efficient and effective PDN optimizing method including many ways to construct a routability-driven PDN. Our method includes a deforming step, two inserting steps and a sizing step to modify the topology of a PDN. We adopt the row-style based PDN for the deforming step to facilitate routabiliy of signal nets. For inserting step, one is a local insertion method which inserts power stripes according to factors that influence IR-drop. The other is a global insertion method which builds rectilinear Steiner minimal trees (RSMTs for short) and applies monotonic routing algorithm to determine the positions of additional power stripes. Compared to the original PDN, the proposed method can not only eliminate voltage violations efficiently but also consume a little power routing resource and induce a little routing congestion. The experimental results show that our method achieves promising results in industry designs.

    摘要 II Abstract III Table of Contents IV List of Tables VI List of Figures VII Chapter 1 Introduction 1 1.1 Previous Works 2 1.2 Our Contributions 4 Chapter 2 Problem Formulation 6 Chapter 3 Preliminary 7 3.1 The Concept of IR-drop 7 3.1.1 Overview of Node-Based Method 9 3.1.2 Fast Voltage Update 10 3.2 Routability 11 3.2.1 Overview of Effective Stripe Width (ESW) 11 3.3 VPS Sizing Method 12 3.3.1 VVM Construction 13 3.3.2 Window Dimension Determination 14 3.3.3 Voltage Violation Hotspot Identification 15 3.3.4 VPSs Sizing 16 Chapter 4 Overview of Our Method 19 4.1 Power Stripe Deforming Method 20 4.1.1 Determination of Positions of Congested Sub-segments 21 4.2 Local Power Stripe Inserting Method 22 4.2.1 Evaluation of Influence on IR-drop in Each Location 23 4.2.2 IR-drop High Related Region Determination 23 4.2.3 Computation of an APRW for Each Region 25 4.2.4 Determination of Positions of VPSs 27 4.3 Global Power Stripe Inserting Method 28 4.3.1 Determination of Positions of VPSs 29 4.3.2 Computation of an APRW for Each Two-pin Net 30 Chapter 5 Experimental Results 31 5.1 Comparison of Results of Steps of Optimizing Method 32 5.2 Comparison of Insertion Methods 33 5.3 Comparison of Our Methodology with Others 34 5.4 IR-drop Map VS. Influence Map 35 5.5 IR-drop Map and Congestion Map 36 Chapter 6 Conclusion 38 Bibliography 39

    C. Alpert, A.B. Kahng, G. Nam, S. Reda, and P. Villarrubia, “A Semi-Persistent Clustering Technique for VLSI Circuit Placement,” in Proc. ISPD, pp. 200-207, Apr. 2005.
    C. Andrew, K. M. Kim, U. Mallappa, S. Sapatnekar, X. Bangqi,“Template-Based PDN Synthesis in Floorplan and Placement Using Classifier and CNN Techniques,” in Proc. ASP-DAC, Jan. 2020.
    W.-H. Chang, M.C.-T. Chao and S.-H. Chen, “Practical Routability-Driven Design Flow for Multilayer Power Networks Using Aluminum-Pad Layer,” in IEEE TVLSI, vol. 22, no. 5, pp. 1069-1081, Jan. 2013.
    V. A. Chhabria, V. Ahuja, A. Prabhu, N. Patil, P. Jain and S. S. Sapatnekar“Thermal and IR Drop Analysis Using Convolutional Encoder-Decoder Networks,” in Proc. ASP-DAC, pp. 690-696, Jan. 2021.
    C. Chu and Y.-C. Wong, “FLUTE: Fast Lookup Table Based Rectilinear Steiner Minimal Tree Algorithm for VLSI Design,” in IEEE TCAD, vol.27, no. 1, pp. 70-83, Jan. 2008.
    Y. Chuang, C. Lin, H. Chen, C. Lee and T. Chen “More Effective Power Network Prototyping by Analytical and Centroid Learning,” in Proc. ISCAS, pp.1-5, May 2019.
    S. Dey, S. Nandi and G. Trivedi, “PowerPlanningDL: Reliability-Aware Framework for On-Chip Power Grid Design Using Deep Learning,” in Proc. DATE, pp. 1520-1525, Mar. 2020.
    Y. Fang, H. Lin, M. Sui, C. Li and E. J. Fang, “Machine-Learning-Based Dynamic IR Drop Prediction for ECO” in Proc. ICCAD, pp.1-7, Nov. 2018.
    C. -T. Ho and A. B. Kahng, “IncPIRD: Fast Learning-Based Prediction of Incremental IR Drop” in Proc. ICCAD, pp. 1-8, Nov. 2019.
    A. B. Kahng, Bao Liu and Qinke Wang, “Supply Voltage Degradation Aware Analytical Placement,” in Proc. ICCD, pp. 437-443, Oct. 2005.
    S. Kose and E. G. Friedman, “Fast Algorithms for IR Voltage Drop Analysis Exploiting Locality,” in Proc. DAC, pp. 996-1001, Jun. 2011.
    J.-M. Lin, Y.-T. Kung, Z.-Y. Huang, I-R. Chen, “A Fast Power Network Optimization Algorithm for Improving Dynamic IR-Drop,” in Proc. ISPD, pp. 91-98, Mar. 2021.
    J.-M. Lin, J.-S. Syu and I.-R. Chen, “Macro-Aware Row-Style Power Delivery Network Design for Better Routability,” in Proc. ICCAD, Nov. 2018.
    S.S.-Y. Liu, C.-J. Lee, C.-C. Huang, H.-M. Chen, C.-T. Lin and C.-H. Lee, “Effective Power Network Prototyping Via Statistical-Based Clustering and Sequential Linear Programming,” in Proc. DATE, pp. 1701-1706, Mar. 2013.
    M. Pan and C. Chu, “FastRoute 2.0: A High-Quality and Efficient Global Router,” in Proc. ASP-DAC, pp. 250-255, May 2007.
    C. -H. Pao, A. -Y. Su and Y. -M. Lee, “XGBIR: An XGBoost-Based IR Drop Predictor for Power Delivery Network,” in Proc. DATE, pp. 1307-1310, Mar. 2020.
    H. Qian, S. R. Nassif, and S. S. Sapatnekar, “Random Walks in a Supply Network,” in Proc. DAC, pp. 93-98, Jun. 2003.
    T.-Y. Wang and C.-C. Chen, “Optimization of the Power/Ground Network Wire-Sizing and Spacing Based on Sequential Network Simplex Algorithm,” in Proc. ISQED, pp. 157-162, Mar. 2002.
    Sheldon X.-D. Tan, C.-J. Richard Shi, and J.-C. Lee, “Reliability-Constrained Area Optimization of VLSI Power/Ground Networks Via Sequence of Linear Programmings,” in IEEE. TACD, pp. 1678-1684, Dec. 2003.
    Z. Xie, H. Ren, B. Khailany, Y. Sheng, S. Santosh, J. Hu, Y. Chen“PowerNet: Transferable Dynamic IR Drop Estimation Via Maximum Convolutional Neural Network,” in Proc. ASP-DAC, Jan. 2020.
    Y. Zhong and M. D. F. Wong, “Fast Algorithms for IR Drop Analysis in Large Power Grid,” in Proc. ICCAD, Nov. 2005.
    Y. Zhong and M. D. F. Wong, “Fast Placement Optimization of Power Supply Pads,” in Proc. ASP-DAC, May. 2007.
    H. Zhou, W. Jin and S. X. . -D. Tan, “GridNet: Fast Data-Driven EM-Induced IR Drop Prediction and Localized Fixing for On-Chip Power Grid Networks,” in Proc. ICCAD, pp. 1-9, Nov. 2020.

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