On the superiority of modularity-based clustering for determining placement-relevant clusters

Fogaça, Mateus; Kahng, Andrew B.; Monteiro, Eder; Reis, Ricardo; Wang, Lutong; Woo, Mingyu

doi:10.1016/j.vlsi.2020.03.007

Cited by 13 publications

(5 citation statements)

References 14 publications

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“…Geng et al [16] developed the fuzzy DSM with a transition closure method to obtain the service modules for a result-oriented PSS. Fogaça et al [36] used a graph model of netlist for component relationship comparison and applied the genetic algorithm (GA) to arrange service modules for electric chips. Sun, J. et al [14] presented the PSS modularization based on functional requirements analysis (FRA) and emphasized the structural similarity principle during modularization.…”

Section: Service Component Cluster Methodsmentioning

confidence: 99%

Modularization Design for Smart Industrial Service Ecosystem: A Framework Based on the Smart Industrial Service Identification Blueprint and Hypergraph Clustering

et al. 2023

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Compared with the conventional industrial product–service system, the smart industrial service ecosystem (SISE) mentioned in this study contains more service activity according to the characteristics of the industrial context, participation of various stakeholders and smart interconnected technologies. This study proposes a detailed modularization design framework for SISE, which can be referenced in various industrial contexts. Firstly, the context-based smart industrial service identification blueprint (SISIB) is proposed to describe the operation model of SISE and identify the service components. The SISIB can ensure that the designers understand the service and work process of the system and improve or carry out the smart industrial service (SIS) component identification. In the case of this article, SIS components from different industrial levels can be systematically identified. Secondly, smart collaboration and sustainable development principles are proposed for measuring the correlation degree among the service components. Considering the complexity and multi-level distribution nature of service components, the hyperedge concept is presented to realize the correlation comparison among the service components, and the evaluation linguistics is applied to handle the decision uncertainties. With this method, the effective correlation comparison between service components can be formed with few hyperedges. Thirdly, the hypergraph clustering theory is applied to define the SISE service module partition. The triangular fuzzy number is first used in hyperedge strength evaluation to comply with the vague linguistics from service design experts. The normalized hypergraph cut principle is realized using the K nearest neighbors (kNN) algorithm, and with this method, the new unified hypergraph and related Laplace matrix can be obtained. Then, the relevant eigenvalue of that Laplace matrix is gained, and the component clustering visualization is realized using the k-means algorithm. After the clustering is performed, several modular design schemes can be gained. In order to select the best modularization scheme, we referenced the modularity concept and realized the quality measurement for the modular design using hypergraph modularity criteria. Regarding these three steps, a detailed modularization case study for a renewable electricity service ecosystem design is presented to verify the viability and feasibility of the study in service modular design. The result showed that the framework in this study can realize the visible and clearance service component identification in a smart connected multi-level industrial context. The modular design scheme based on hypergraph can also achieve high modularity with a more convenient correlation evaluation.

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Section: Service Component Cluster Methodsmentioning

confidence: 99%

Modularization Design for Smart Industrial Service Ecosystem: A Framework Based on the Smart Industrial Service Identification Blueprint and Hypergraph Clustering

et al. 2023

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“…Compared to MC1, we wish to avoid computationally expensive resampling iterations and define an explicit score function to stabilize the original local optimization steps and cooling strategy. On the other hand, in contrast to the popular modularity metric [ 29 – 32 ] and its different flavors, in MC2, we wish to discourage inclusion of a node in a partition if its internal connectivity is very asymmetric (e.g., only outgoing edges to members of the metacell).…”

Section: Methodsmentioning

confidence: 99%

Metacell-2: a divide-and-conquer metacell algorithm for scalable scRNA-seq analysis

et al. 2022

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Scaling scRNA-seq to profile millions of cells is crucial for constructing high-resolution maps of transcriptional manifolds. Current analysis strategies, in particular dimensionality reduction and two-phase clustering, offer only limited scaling and sensitivity to define such manifolds. We introduce Metacell-2, a recursive divide-and-conquer algorithm allowing efficient decomposition of scRNA-seq datasets of any size into small and cohesive groups of cells called metacells. Metacell-2 improves outlier cell detection and rare cell type identification, as shown with human bone marrow cell atlas and mouse embryonic data. Metacell-2 is implemented over the scanpy framework for easy integration in any analysis pipeline.

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“…Legalization is the step where the cells are moved to eliminate overlapping. Detailed placement is the step where is done local placement optimizations with different goals: optimization of wire length, elimination of timing violations, reduction of power consumption, improvement of routability, and also manufacturability [14] [15]. The quality of the placement is fundamental to improving the routing quality.…”

Section: ) Placement Toolsmentioning

confidence: 99%

EDA: Overview and Some Trends

Reis¹

2022

JICS

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The development of any integrated circuit depends heavily on the quality of the EDA (Electronic Design Automation) tools used in the design flow. Improved CAD tools and algo-rithms are needed to cope with new fabrication technology requirements, advanced performance constraints, or simply the enormous number of elements involved. In this paper it is giving an overview of the importance of automation in the design process, and it is done a survey of different types of tools presenting some classifications of the tools. Then some few trends on EDA that are needed to deal with the evolution of manufacturing processes will be presented. Some optimi-zation techniques will be presented for selected physical de-sign problems (layout). An important aspect of the design is to reduce power consumption at all levels of abstraction. Power optimization is fundamental in nanoCMOS and in the IoT world. At logic and physical levels, one approach that can be used to optimize the circuit, especially reducing static leakage power and using the automatic generation of the cell layout. With on-the-fly cell generation, the same function can be implemented with a reduced number of transistors, requiring less area and significantly optimizing power and performance. Finally, the use of estimation and visualiza-tion tools is equally important. They can be applied either in the design flow or in the tool's development and research en-vironments as a way to observe and understand the behavior and interactions of algorithms and their operation on real designs and benchmarks.

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On the superiority of modularity-based clustering for determining placement-relevant clusters

Cited by 13 publications

References 14 publications

Modularization Design for Smart Industrial Service Ecosystem: A Framework Based on the Smart Industrial Service Identification Blueprint and Hypergraph Clustering

Modularization Design for Smart Industrial Service Ecosystem: A Framework Based on the Smart Industrial Service Identification Blueprint and Hypergraph Clustering

Metacell-2: a divide-and-conquer metacell algorithm for scalable scRNA-seq analysis

EDA: Overview and Some Trends

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