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Second-Order Arnoldi Reduction Using Weighted Gaussian Kernel

تخفيض أرنولدي من الدرجة الثانية باستخدام نواة جاوس المرجحة
Authors: Rahila Malik; Mehboob Alam; Shah Muhammad; Faisal Z. Duraihem; Yehia Massoud;

Second-Order Arnoldi Reduction Using Weighted Gaussian Kernel

Abstract

La modélisation et la conception de l'interconnexion sur puce continuent d'être un obstacle fondamental pour l'électronique à grande vitesse. La mise à l'échelle continue des dispositifs et des interconnexions sur puce génère des selfs et des inductances mutuelles, entraînant la génération de systèmes dynamiques de second ordre. La réduction de la commande de modèle est une partie essentielle de tout outil de conception assistée par ordinateur moderne pour la vérification de la préfabrication dans la conception de composants et d'interconnexions sur puce. Les méthodes de réduction de second ordre existantes utilisent une inversion de matrice coûteuse pour générer des matrices de projection orthogonales et souvent ne préservent pas la stabilité et la passivité du système d'origine. Dans ce travail, une méthode de réduction d'Arnoldi de second ordre est proposée, qui choisit sélectivement les points d'interpolation pondérés avec un noyau gaussien dans la gamme donnée de fréquences d'intérêt pour générer la matrice de projection. Le procédé proposé assure la stabilité et la passivité du modèle d'ordre réduit sur la plage de fréquences souhaitée. Les résultats de la simulation montrent que la combinaison de points multi-déplacements pondérés avec le noyau gaussien et la projection sélective en fréquence génère dynamiquement des résultats optimaux avec une meilleure précision et stabilité numérique par rapport aux techniques de réduction existantes.

El modelado y el diseño de la interconexión en el chip siguen siendo un obstáculo fundamental para la electrónica de alta velocidad. El escalado continuo de los dispositivos y las interconexiones en el chip genera inductancias propias y mutuas, lo que resulta en la generación de sistemas dinámicos de segundo orden. La reducción de pedidos de modelos es una parte esencial de cualquier herramienta moderna de diseño asistido por ordenador para la verificación de prefabricación en el diseño de componentes e interconexiones en chip. Los métodos de reducción de segundo orden existentes utilizan una costosa inversión de matriz para generar matrices de proyección ortogonales y, a menudo, no conservan la estabilidad y la pasividad del sistema original. En este trabajo, se propone un método de reducción de Arnoldi de segundo orden, que selecciona selectivamente los puntos de interpolación ponderados con un núcleo gaussiano en el rango dado de frecuencias de interés para generar la matriz de proyección. El método propuesto garantiza la estabilidad y la pasividad del modelo de orden reducido en el rango de frecuencia deseado. Los resultados de la simulación muestran que la combinación de puntos de desplazamiento múltiple ponderados con núcleo gaussiano y proyección selectiva de frecuencia genera dinámicamente resultados óptimos con mejor precisión y estabilidad numérica en comparación con las técnicas de reducción existentes.

Modeling and design of on-chip interconnect continue to be a fundamental roadblock for high-speed electronics. The continuous scaling of devices and on-chip interconnects generates self and mutual inductances, resulting in generating second-order dynamical systems. The model order reduction is an essential part of any modern computer-aided design tool for prefabrication verification in the design of on-chip components and interconnects. The existing second-order reduction methods use expensive matrix inversion to generate orthogonal projection matrices and often do not preserve the stability and passivity of the original system. In this work, a second-order Arnoldi reduction method is proposed, which selectively picks the interpolation points weighted with a Gaussian kernel in the given range of frequencies of interest to generate the projection matrix. The proposed method ensures stability and passivity of the reduced-order model over the desired frequency range. The simulation results show that the combination of multi-shift points weighted with Gaussian kernel and frequency selective projection dynamically generates optimal results with better accuracy and numerical stability compared to existing reduction techniques.

لا تزال نمذجة وتصميم الاتصال البيني على الشريحة حاجزًا أساسيًا أمام الإلكترونيات عالية السرعة. يؤدي التوسع المستمر للأجهزة والوصلات البينية على الرقاقة إلى توليد محاثات ذاتية ومتبادلة، مما يؤدي إلى توليد أنظمة ديناميكية من الدرجة الثانية. يعد تقليل ترتيب النموذج جزءًا أساسيًا من أي أداة تصميم حديثة بمساعدة الكمبيوتر للتحقق من التصنيع المسبق في تصميم المكونات والوصلات البينية على الرقاقة. تستخدم طرق الاختزال من الدرجة الثانية الحالية انعكاسًا مصفوفيًا مكلفًا لتوليد مصفوفات إسقاط متعامدة وغالبًا ما لا تحافظ على استقرار وسلبية النظام الأصلي. في هذا العمل، يتم اقتراح طريقة تقليل أرنولدي من الدرجة الثانية، والتي تختار بشكل انتقائي نقاط الاستيفاء المرجحة بنواة جاوس في نطاق معين من الترددات محل الاهتمام لتوليد مصفوفة الإسقاط. تضمن الطريقة المقترحة استقرار وسلبية النموذج منخفض الترتيب على نطاق التردد المطلوب. تُظهر نتائج المحاكاة أن الجمع بين نقاط الإزاحة المتعددة الموزونة بنواة جاوس والإسقاط الانتقائي للتردد يولد بشكل ديناميكي نتائج مثالية بدقة أفضل وثبات رقمي مقارنة بتقنيات الاختزال الحالية.

Country
Saudi Arabia
Keywords

Artificial intelligence, On-Chip Interconnects, Geometry, Control (management), Second-order Arnoldi, Quantum mechanics, Engineering, numerical methods, Machine learning, FOS: Electrical engineering, electronic engineering, information engineering, Electromagnetic Compatibility in Electronics, Control theory (sociology), FOS: Mathematics, Model Reduction, Electrical and Electronic Engineering, Stability (learning theory), Physics-Informed Neural Networks for Scientific Computing, Interpolation points, Physics, Projection (relational algebra), Second-order model order reduction, Statistical and Nonlinear Physics, Computer science, second-order Arnoldi, TK1-9971, Algorithm, second-order model order reduction, Physics and Astronomy, High-Frequency Modeling, Combinatorics, Physical Sciences, Gaussian, Model order reduction, Kernel (algebra), Finite-Difference Time-Domain Methods in Electromagnetics, on-chip interconnects, Reduction (mathematics), Numerical methods, Electrical engineering. Electronics. Nuclear engineering, High-Order Methods, Mathematics

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
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gold