Purdue-Artificial-Intelligence-in-Music/Evaluator-code
Oct 2025 – Oct 2025
1 Month active
Languages Used
GradleJavaKotlinMarkdownPython
Technical Skills
Android DevelopmentBuild ConfigurationDocumentationEmbedded SystemsFirebaseMachine Learning
ShriPeru
PROFILE
Shriperu
Shrinidhi Perumal developed NPU and Quantized Neural Network acceleration for Android app components in the Purdue-Artificial-Intelligence-in-Music/Evaluator-code repository, focusing on the bow and camera modules to enable faster on-device model inference. Leveraging Kotlin, Java, and Gradle, Shrinidhi integrated Firebase App Distribution into the deployment workflow, streamlining testing and release cycles for mobile development. The work included updating NPU setup documentation in Markdown, reducing onboarding time for new contributors and aligning deployment readiness with improved tooling. This month’s engineering effort emphasized performance optimization and deployment efficiency, demonstrating depth in embedded systems, model optimization, and build configuration without requiring major bug fixes.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
585
Activity Months1
Your Network
6 people
Work History
October 2025
2 Commits • 1 Features
Oct 1, 2025Monthly summary for 2025-10: Delivered NPU/QNN acceleration for Android app components (bow and camera), enabling faster on-device model inference, and wired Firebase App Distribution into the deployment workflow to streamline testing and releases. Updated NPU setup documentation to reduce onboarding time. No major bugs were reported this month; the focus was on performance and deployment readiness. Overall impact includes improved user-perceived performance, faster iteration cycles, and clearer setup guidance for developers.
2 Commits • 1 Features
Oct 1, 2025Monthly summary for 2025-10: Delivered NPU/QNN acceleration for Android app components (bow and camera), enabling faster on-device model inference, and wired Firebase App Distribution into the deployment workflow to streamline testing and releases. Updated NPU setup documentation to reduce onboarding time. No major bugs were reported this month; the focus was on performance and deployment readiness. Overall impact includes improved user-perceived performance, faster iteration cycles, and clearer setup guidance for developers.
October 2025
Activity
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Quality Metrics
Correctness85.0%
Maintainability80.0%
Architecture85.0%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
GradleJavaKotlinMarkdownPython
Technical Skills
Android DevelopmentBuild ConfigurationDocumentationEmbedded SystemsFirebaseMachine LearningMobile DevelopmentModel OptimizationNPU Integration
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