google/XNNPACK
Jan 2025 – Mar 2025
3 Months active
Languages Used
CCMakePythonShellStarlark
Technical Skills
Assembly Language (implied by RVV intrinsics)BenchmarkingC ProgrammingEmbedded SystemsPerformance OptimizationRISC-V
Gary Yi-Hung Chen
PROFILE
Gary Yi-hung Chen
Gary contributed to google/XNNPACK by developing and optimizing RISC-V Vector (RVV) accelerated kernels for sparse matrix multiplication and convolution over a three-month period. He implemented RVV-based microkernels in C and assembly, targeting various matrix and convolution dimensions to enhance inference throughput on RVV-enabled hardware. His work included updating build systems, introducing comprehensive benchmarks and tests, and addressing reliability through overflow prevention and vector initialization fixes. Gary also improved code maintainability by refining code generation and formatting practices. These efforts deepened hardware support for RISC-V architectures and advanced the performance and robustness of XNNPACK’s low-level computational kernels.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
8Total
Bugs
0
Commits
8
Features
3
Lines of code
21,846
Activity Months3
Your Network
52 people
Work History
March 2025
5 Commits • 1 Features
Mar 1, 2025March 2025 performance-focused month for google/XNNPACK, delivering key RVV depthwise convolution improvements with reliability and codebase maintenance. Achieved substantial speedups through new microkernels and loop unrolling, enhanced robustness by addressing overflow risks and vector initialization issues, and streamlined generated-code maintenance via header path rewrites and clang-format controls. These efforts improve inference throughput on selected hardware and strengthen maintainability for future vectorization work.
5 Commits • 1 Features
Mar 1, 2025March 2025 performance-focused month for google/XNNPACK, delivering key RVV depthwise convolution improvements with reliability and codebase maintenance. Achieved substantial speedups through new microkernels and loop unrolling, enhanced robustness by addressing overflow risks and vector initialization issues, and streamlined generated-code maintenance via header path rewrites and clang-format controls. These efforts improve inference throughput on selected hardware and strengthen maintainability for future vectorization work.
March 2025
February 2025
2 Commits • 1 Features
Feb 1, 2025February 2025 focused on delivering performance- and portability-oriented kernel optimizations for RVV on XNNPACK. Delivered new RVV-accelerated f32 convolution and depthwise convolution kernels, with accompanying C sources, tests, and build-system updates to integrate these kernels into the MLOps-friendly build and test pipelines. This work extends hardware support for RISC-V vector architectures and sets the foundation for higher throughput on edge devices.
February 2025
2 Commits • 1 Features
Feb 1, 2025February 2025 focused on delivering performance- and portability-oriented kernel optimizations for RVV on XNNPACK. Delivered new RVV-accelerated f32 convolution and depthwise convolution kernels, with accompanying C sources, tests, and build-system updates to integrate these kernels into the MLOps-friendly build and test pipelines. This work extends hardware support for RISC-V vector architectures and sets the foundation for higher throughput on edge devices.
January 2025
1 Commits • 1 Features
Jan 1, 20252025-01 monthly summary for google/XNNPACK: Delivered RVV-based f32 SPMM kernel support, expanding sparse matrix multiplication acceleration to RVV-enabled hardware. Implemented micro-kernels for dims: 1x1, 1x2, 1x4, 2x1, 2x2, 2x4, 4x1, 4x2, 4x4, 8x1, 8x2, 8x4, with build-system updates and accompanying benchmarks and tests to validate performance and correctness on RVV-enabled devices.
1 Commits • 1 Features
Jan 1, 20252025-01 monthly summary for google/XNNPACK: Delivered RVV-based f32 SPMM kernel support, expanding sparse matrix multiplication acceleration to RVV-enabled hardware. Implemented micro-kernels for dims: 1x1, 1x2, 1x4, 2x1, 2x2, 2x4, 4x1, 4x2, 4x4, 8x1, 8x2, 8x4, with build-system updates and accompanying benchmarks and tests to validate performance and correctness on RVV-enabled devices.
January 2025
Activity
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Quality Metrics
Correctness96.2%
Maintainability92.6%
Architecture93.8%
Performance95.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CCMakePythonShellStarlark
Technical Skills
Assembly LanguageAssembly Language (implied by RVV intrinsics)BenchmarkingBuild SystemsC ProgrammingC programmingCode GenerationDeep Learning FrameworksEmbedded SystemsEmbedded systemsLow-level OptimizationLow-level optimizationPerformance EngineeringPerformance OptimizationPerformance Tuning
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