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研究生: 黃兆源
Huang, Chao-Yuan
論文名稱: 基於絕熱量子通量超導電路之分離器和緩衝器最佳插入算法
An Optimal Algorithm for Splitter and Buffer Insertion in Adiabatic Quantum-Flux-Parametron Circuits
指導教授: 蔡明哲
Tsai, Ming-Jer
口試委員: 何宗易
郭桐惟
郭建志
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 資訊工程學系
Computer Science
論文出版年: 2021
畢業學年度: 109
語文別: 英文
論文頁數: 30
中文關鍵詞: 絕熱量子通量參變器超導電路緩衝器分離器動態規劃
外文關鍵詞: Adiabatic Quantum-Flux-Parametron, AQFP, Buffer, Splitter, Dynamic Programming
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  • 絕熱量子通量超導電路(AQFP) 是新興的超導電路之一,擁有低功耗和資料快速切換等優良特性。為了能快速切換資料,AQFP的邏輯閘對其輸入延遲有嚴格要求,因此需要額外的緩衝器來同步延遲。同時,為了保持邏輯閘的對稱佈局並減少不需要的寄生電容以及磁耦合,AQFP 單元庫採用極簡設計方法,其中有多個扇出的邏輯閘必須使用分離器。因此,AQFP 電路可能需要大量的分離器和緩衝器,從而導致大量的功耗和延遲。這使得為AQFP 電路提出有效的分離器和緩衝器插入算法成為我們的動機。在本論文中,我們提出了一種基於動態規劃的算法,該算法為輸入電路的每條線提供了最佳分離器和緩衝器插入。實驗結果表明,我們的方法擁有高效率,與最新方法相比,較為複雜電路中額外的約瑟夫森結(JJ) 減少了10%。


    The Adiabatic Quantum-Flux-Parametron (AQFP), which benefits from low power consumption and rapid switching, is one of the rising superconducting logics. Due to the rapid switching, the delay of the inputs of an AQFP gate is strictly specified so that additional buffers are needed to synchronize the delay. Meanwhile, to maintain the symmetry layout of gates and reduce the undesired parasitic magnetic coupling, the AQFP cell library adopts the minimalist design method in which splitters are employed for the gates with multiple fan-outs. Thus, an AQFP circuit may demand numerous splitters and buffers, resulting in a considerable amount of power consumption and delay. This provides a motivation for proposing an effective splitter and buffer insertion algorithm for the AQFP circuits. In this thesis, we propose a dynamic programming-based algorithm that provides an optimal splitter and buffer insertion for each wire of the input circuit. Experimental results show that our method is fast, and has a 10% reduction of additional Josephson Junctions (JJs) in the complicated circuits compared with the state-of-the-art method.

    誌謝 摘要 i Abstract ii 1 Introduction 1 2 Related Works 5 3 The Algorithm for the Optimal Buffers Insertion Problem Of Single wire 9 3.1 The Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.2 The Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4 The Analysis 15 5 Experimental Results 21 5.1 The Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 5.2 The Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 6 Conclusion 27 References 29

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