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 low-level kernels and infrastructure for Hexagon HVX and cross-architecture backends. Over five months, Eunj delivered HVX-accelerated f32-gemm and IGEMM microkernels, enhanced benchmarking suites, and improved type safety and portability across C++ and C codebases. Their work included refactoring elementwise operations for consistent int32_t usage, stabilizing Hexagon builds by addressing template type deduction, and modernizing kernel clamping and min/max handling. By integrating assembly-level optimizations, cross-compilation, and robust testing, Eunj enabled reliable, high-performance computation and streamlined maintenance for embedded and SIMD-optimized neural network inference workflows.
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
198 people
Work 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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