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
Worked on the Purdue-Artificial-Intelligence-in-Music/Evaluator-code repository to deliver NPU and Quantized Neural Network acceleration for Android app components, specifically targeting bow and camera modules to enhance on-device model inference speed. Leveraged Kotlin and Java to integrate these optimizations, focusing on efficient use of embedded systems and machine learning techniques. Wired Firebase App Distribution into the deployment workflow, streamlining testing and release processes for mobile development. Updated the NPU setup documentation in Markdown, reducing onboarding time for new contributors. The work emphasized deployment readiness, improved user-perceived performance, and provided clearer guidance for future development and faster iteration cycles.
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 peopleWork 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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