google/XNNPACK
Jan 2025 – Mar 2026
4 Months active
Languages Used
CCMakePythonShellStarlarkC++
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, targeting improved inference throughput on RVV-enabled hardware. He implemented microkernels in C and assembly, unrolled loops for depthwise convolution, and addressed numerical stability and initialization issues to enhance reliability. Gary integrated these kernels into the build system, added benchmarks and tests for validation, and maintained code quality through formatting and header management. He also fixed a benchmarking bug in the GEMM path using C++, improving cache usage consistency. His work demonstrated depth in low-level optimization and performance engineering.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
9Total
Bugs
1
Commits
9
Features
3
Lines of code
21,854
Activity Months4
Your Network
69 peopleWork History
March 2026
1 Commits
Mar 1, 2026March 2026 monthly summary for google/XNNPACK: Delivered a critical correctness fix in the GEMM benchmarking path by ensuring the correct buffer is prefetched, improving cache usage reliability and benchmarking integrity. This change reduces variance in benchmark results and supports more reliable performance decisions for downstream optimization and product planning.
1 Commits
Mar 1, 2026March 2026 monthly summary for google/XNNPACK: Delivered a critical correctness fix in the GEMM benchmarking path by ensuring the correct buffer is prefetched, improving cache usage reliability and benchmarking integrity. This change reduces variance in benchmark results and supports more reliable performance decisions for downstream optimization and product planning.
March 2026
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.
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.
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.
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
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.
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.
Activity
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Quality Metrics
Correctness96.6%
Maintainability93.4%
Architecture94.4%
Performance95.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
CC++CMakePythonShellStarlark
Technical Skills
Assembly LanguageAssembly Language (implied by RVV intrinsics)BenchmarkingBuild SystemsC ProgrammingC programmingC++ programmingCode GenerationDeep Learning FrameworksEmbedded SystemsEmbedded systemsLow-level OptimizationLow-level optimizationPerformance EngineeringPerformance Optimization
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