google/XNNPACK
Nov 2024 – Jun 2025
5 Months active
Languages Used
C++CCMakeCMakeScriptPythonStarlarkShell
Technical Skills
C++Code RefactoringSoftware DevelopmentSoftware EngineeringTestingAssembly (Implicit via HVX intrinsics)
EJ Park
PROFILE
Ej Park
Eunj Park contributed to the google/XNNPACK repository by developing and optimizing HVX-accelerated f32-gemm and IGEMM kernel families, focusing on cross-architecture reliability and performance for embedded systems. Using C++ and C, Eunj standardized integer typing with int32_t across elementwise operations, improving type safety and reducing runtime errors. They enhanced benchmarking suites, modernized clamping and min/max handling for numerical stability, and introduced portable APIs to support diverse hardware targets. Eunj also stabilized Hexagon build paths by addressing template type deduction issues, ensuring reliable compilation and integration testing. Their work demonstrated depth in low-level optimization, SIMD programming, and build system maintenance.
Overall Statistics
Feature vs Bugs
56%Features
Repository Contributions
24Total
Bugs
4
Commits
24
Features
5
Lines of code
5,073
Activity Months5
Your Network
228 peopleWork History
June 2025
1 Commits
Jun 1, 2025June 2025 monthly summary for google/XNNPACK focusing on stability and build reliability for Hexagon backend. Delivered a targeted fix to ensure Hexagon build success by explicitly specifying the std::max template argument as int32_t in fp32-transformer.cc and qd8-transformer.cc, enabling reliable cross-architecture validation and faster integration testing. Commit: 12d8653323952ecb637d49812e87285c32a98353.
1 Commits
Jun 1, 2025June 2025 monthly summary for google/XNNPACK focusing on stability and build reliability for Hexagon backend. Delivered a targeted fix to ensure Hexagon build success by explicitly specifying the std::max template argument as int32_t in fp32-transformer.cc and qd8-transformer.cc, enabling reliable cross-architecture validation and faster integration testing. Commit: 12d8653323952ecb637d49812e87285c32a98353.
June 2025
April 2025
7 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for google/XNNPACK: Delivered cohesive HVX IGEMM kernel ecosystem enhancements for f32_igemm, added performance benchmarks, and completed maintenance to stabilize support for Hexagon V73.
April 2025
7 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for google/XNNPACK: Delivered cohesive HVX IGEMM kernel ecosystem enhancements for f32_igemm, added performance benchmarks, and completed maintenance to stabilize support for Hexagon V73.
March 2025
8 Commits • 2 Features
Mar 1, 2025Concise monthly summary for 2025-03 focusing on key accomplishments, major fixes, impact, and skills demonstrated for google/XNNPACK.
8 Commits • 2 Features
Mar 1, 2025Concise monthly summary for 2025-03 focusing on key accomplishments, major fixes, impact, and skills demonstrated for google/XNNPACK.
March 2025
February 2025
6 Commits • 2 Features
Feb 1, 2025February 2025 — Google/XNNPACK: HVX-accelerated f32-gemm kernel family delivered with portable API and activation support; HVX benchmarking suite enhanced and cleaned; gemm-config changes reverted pending kernel version; overall impact: measurable performance gains on HVX hardware, improved API portability, and increased maintainability for future kernel iterations.
February 2025
6 Commits • 2 Features
Feb 1, 2025February 2025 — Google/XNNPACK: HVX-accelerated f32-gemm kernel family delivered with portable API and activation support; HVX benchmarking suite enhanced and cleaned; gemm-config changes reverted pending kernel version; overall impact: measurable performance gains on HVX hardware, improved API portability, and increased maintainability for future kernel iterations.
November 2024
2 Commits
Nov 1, 2024November 2024 monthly summary for google/XNNPACK focused on cross-architecture reliability and typing safety for elementwise operations. Implemented standardized int32_t usage across architectures to prevent type-related errors, added necessary overloads and type-safe casts, and updated tests to reflect the new inputs. These changes reduce runtime errors, simplify cross-platform maintenance, and improve predictability of results across Hexagon, ARM, and x86 targets.
2 Commits
Nov 1, 2024November 2024 monthly summary for google/XNNPACK focused on cross-architecture reliability and typing safety for elementwise operations. Implemented standardized int32_t usage across architectures to prevent type-related errors, added necessary overloads and type-safe casts, and updated tests to reflect the new inputs. These changes reduce runtime errors, simplify cross-platform maintenance, and improve predictability of results across Hexagon, ARM, and x86 targets.
November 2024
Activity
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Quality Metrics
Correctness95.0%
Maintainability91.6%
Architecture90.8%
Performance90.8%
AI Usage20.0%
Skills & Technologies
Programming Languages
CC++CMakeCMakeScriptPythonShellStarlark
Technical Skills
Assembly (Implicit via HVX intrinsics)Assembly LanguageBenchmarkingBuild SystemBuild SystemsC ProgrammingC++C++ DevelopmentCode CleanupCode GenerationCode MaintenanceCode RefactoringCross-CompilationEmbedded SystemsEmbedded Systems Development
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