Design and implementation of a web UI for data visualization in data-driven tools
Takami, Tomoko (2025)
2025
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025110427036
https://urn.fi/URN:NBN:fi:amk-2025110427036
Tiivistelmä
This thesis presents the design and implementation of a metadata-driven user interface for visualizing RTL-level power analysis in a modern System-on-Chip (SoC) design workflow. The tool integrates with Nokia’s internal power analysis infrastructure and was developed to address the needs of engineers working across different abstraction levels—from IP, subsystem, to top-level design.
The UI was implemented using React and Redux Toolkit and adheres to Nokia’s internal design system (NDS). Metadata from uploaded analysis results dynamically populates filtering options such as program, project, instance, and scenario. The interface supports trend visualization, block-level power distribution, and sortable data tables using AG Grid components.
The development followed an iterative process involving direct feedback from multiple IP teams. As a result, the UI was refined to support scenario-based filtering, improved metadata handling, and consistent terminology across teams. Initial evaluations using legacy data suggest that the system reduces manual workload, standardizes access to power metrics, and improves early visibility into regressions.
Although the system is currently undergoing staged integration, the architecture is scalable and adaptable to other domains beyond power analysis. Future extensions include role-based dashboards and visualization of test and verification workflows.
The UI was implemented using React and Redux Toolkit and adheres to Nokia’s internal design system (NDS). Metadata from uploaded analysis results dynamically populates filtering options such as program, project, instance, and scenario. The interface supports trend visualization, block-level power distribution, and sortable data tables using AG Grid components.
The development followed an iterative process involving direct feedback from multiple IP teams. As a result, the UI was refined to support scenario-based filtering, improved metadata handling, and consistent terminology across teams. Initial evaluations using legacy data suggest that the system reduces manual workload, standardizes access to power metrics, and improves early visibility into regressions.
Although the system is currently undergoing staged integration, the architecture is scalable and adaptable to other domains beyond power analysis. Future extensions include role-based dashboards and visualization of test and verification workflows.