Ultragraph Model for ECE Component Partitioning

The article considers the modelling of a computational device at the design stage. One of the most labour-intensive problems is a partitioning problem which belongs to the class of NP-hard problems. In other words, there is no precise method for its addressing. The authors suggest an alternative way to model the device circuits as an ultragraph which simulates circuit components taking into account a direction of signal transmission. Thus, the suggested approach makes it possible to obtain an adequate model in terms of the correctness of information and its completeness. As an example, an ultragraph model of an amplifier is given both graphically and analytically. Thу ultragraph model is firstly adopted to ECE components partitioning problem. A problem statement is considered on the basis of the ultragraph model. A new encoding and decoding mechanism is developed to address the partitioning problem by a bioinspired algorithm. To confirm its effectiveness, a software is developed. The goal of the experiments is a calculation of CPU time and memory as well as of the comparison the ultragraph model with graph and hypergraph models. It is experimentally proved that the ultragraph model can reduce CPU time cost in comparison with other mathematical models.

[1]  Sergey P. Malioukov,et al.  General Questions of Automated Design and Engineering , 2009 .

[2]  Vladimir V. Kureichik,et al.  Partitioning of ECE schemes components based on modified graph coloring algorithm , 2014, Proceedings of IEEE East-West Design & Test Symposium (EWDTS 2014).

[4]  K. Passino,et al.  Biomimicry of Social Foraging Bacteria for Distributed Optimization: Models, Principles, and Emergent Behaviors , 2002 .

[5]  T. Bates,et al.  $C^{\ast }$ -algebras of labelled graphs III— $K$ -theory computations , 2012, Ergodic Theory and Dynamical Systems.

[6]  Y. Liu,et al.  Biomimicry of Social Foraging Bacteria for Distributed Optimization : Models , Principles , and Emergent Behaviors 1 , 2002 .

[7]  Igor Koltchanov,et al.  Optical interconnects for datacenter links: design and modeling challenges , 2020, OPTO.

[8]  Vladimir Kureichik,et al.  Hybryd Approach for Computer-Aided Design Problems , 2019, 2019 International Seminar on Electron Devices Design and Production (SED).

[9]  Vladimir V. Kureichik,et al.  Hybrid Approach for Graph Partitioning , 2017, CSOC.

[10]  D.J. Allstot,et al.  A fully integrated 0.5-5.5 GHz CMOS distributed amplifier , 2000, IEEE Journal of Solid-State Circuits.

[11]  O A ] 2 5 O ct 2 01 7 KMS and ground states on ultragraph C *-algebras October 26 , 2017 , 2017 .

[12]  Jin Hu,et al.  Progress and Challenges in VLSI Placement Research , 2012, Proceedings of the IEEE.

[13]  Elmar Kuliev,et al.  Mechanisms of swarm intelligence and evolutionary adaptation for solving PCB design tasks , 2019, 2019 International Seminar on Electron Devices Design and Production (SED).

[14]  Daria V. Zaruba,et al.  Parametric Optimization Based on Bacterial Foraging Optimization , 2017, CSOC.

[15]  Evolutionary Algorithm for Extremal Subsets Comprehension in Graphs , 2013 .

[16]  Danna Zhou,et al.  d. , 1934, Microbial pathogenesis.

[17]  M. Tomforde,et al.  Graph algebras, Exel-Laca algebras, and ultragraph algebras coincide up to Morita equivalence , 2008, 0809.0164.

[18]  Liying Wang,et al.  An effective bacterial foraging optimizer for global optimization , 2016, Inf. Sci..

[19]  Yasnitsky Leonid Advances in Intelligent Systems and Computing , 2019 .

[20]  Sachin S. Sapatnekar,et al.  Handbook of Algorithms for Physical Design Automation , 2008 .

[21]  Daria Zaruba,et al.  Generation of bioinspired search procedures for optimization problems , 2016, 2016 IEEE 10th International Conference on Application of Information and Communication Technologies (AICT).