google/XNNPACK
Nov 2024 – Jan 2026
6 Months active
Languages Used
C++CCMakeCMakeScriptPythonStarlarkShellMarkdown
Technical Skills
C++Code RefactoringSoftware DevelopmentSoftware EngineeringTestingAssembly (Implicit via HVX intrinsics)
EJ Park
PROFILE
Ej Park
Worked on the google/XNNPACK repository to deliver optimized neural network inference for Hexagon-enabled devices, focusing on low-level performance engineering and cross-architecture reliability. Developed and maintained HVX-accelerated f32-gemm and IGEMM kernels, introduced portable APIs, and enhanced benchmarking suites using C++ and C. Addressed type safety and build stability by standardizing int32_t usage and refining build system integration, particularly for Hexagon V73. Improved code portability and maintainability through code refactoring, documentation updates, and test coverage expansion. The work enabled reliable cross-platform deployment, faster integration testing, and laid the foundation for future Hexagon-specific optimizations in embedded machine learning applications.
Overall Statistics
Feature vs Bugs
60%Features
Repository Contributions
25Total
Bugs
4
Commits
25
Features
6
Lines of code
5,074
Activity Months6
Your Network
241 peopleWork History
January 2026
1 Commits • 1 Features
Jan 1, 2026January 2026 performance summary for google/XNNPACK: Delivered Hexagon architecture support enabling optimized neural network inference on Hexagon-enabled devices; updated README to reflect available architectures; laid groundwork for future Hexagon-specific optimizations and broader device coverage. This work improves on-device inference performance and expands market reach for XNNPACK.
1 Commits • 1 Features
Jan 1, 2026January 2026 performance summary for google/XNNPACK: Delivered Hexagon architecture support enabling optimized neural network inference on Hexagon-enabled devices; updated README to reflect available architectures; laid groundwork for future Hexagon-specific optimizations and broader device coverage. This work improves on-device inference performance and expands market reach for XNNPACK.
January 2026
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.
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.
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.
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
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.
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.
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.
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
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.
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.
Activity
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Quality Metrics
Correctness95.2%
Maintainability92.0%
Architecture91.2%
Performance91.2%
AI Usage20.0%
Skills & Technologies
Programming Languages
CC++CMakeCMakeScriptMarkdownPythonShellStarlark
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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