google/XNNPACK
Dec 2024 – Apr 2026
9 Months active
Languages Used
CCMakeShellC++CMakeScriptStarlarkBzlPython
Technical Skills
Deep Learning OptimizationEmbedded SystemsPerformance OptimizationQuantizationRISC-VVectorization
Ken Unger
PROFILE
Ken Unger
Ken Unger developed and optimized quantized and floating-point kernels for the google/XNNPACK repository, focusing on RISC-V Vector (RVV) acceleration to improve inference speed and energy efficiency on embedded systems. He engineered microkernels for GEMM, depthwise convolution, and vector operations, addressing both performance and correctness through low-level C and C++ programming, assembly, and rigorous benchmarking. Ken enhanced hardware portability by refining build systems and configuration logic, while also fixing edge-case bugs in quantization and floating-point handling. His work demonstrated depth in low-level optimization, robust testing, and maintainability, resulting in reliable, high-performance math kernels for diverse hardware targets.
Overall Statistics
Feature vs Bugs
47%Features
Repository Contributions
46Total
Bugs
10
Commits
46
Features
9
Lines of code
3,207,309
Activity Months9
Your Network
56 peopleWork History
April 2026
2 Commits
Apr 1, 2026Month 2026-04: Concentrated on hardening XNNPACK’s hardware probing for RISCV targets. Delivered a robustness fix for RISCV_HWPROBE_EXT_ZVFH macro when undefined and aligned documentation with hardware configuration, improving reliability and maintainability without API changes.
2 Commits
Apr 1, 2026Month 2026-04: Concentrated on hardening XNNPACK’s hardware probing for RISCV targets. Delivered a robustness fix for RISCV_HWPROBE_EXT_ZVFH macro when undefined and aligned documentation with hardware configuration, improving reliability and maintainability without API changes.
April 2026
March 2026
14 Commits • 2 Features
Mar 1, 2026March 2026 monthly summary for google/XNNPACK: Delivered RVV-enabled kernels and FP operation enhancements for RISC-V, expanding quantized matmul performance (qd8-f16) and FP16/FP32 support with softmax optimizations; extended RVV reductions and vectorized paths for f16/f32 operations; CI/build system upgrades to support broader architectures; bug fix improving GEMM f16-qb4w error-check reliability; and targeted code/script cleanups to improve maintainability and review efficiency. These efforts advance hardware-accelerated performance, robustness, and release readiness on RISC-V platforms.
March 2026
14 Commits • 2 Features
Mar 1, 2026March 2026 monthly summary for google/XNNPACK: Delivered RVV-enabled kernels and FP operation enhancements for RISC-V, expanding quantized matmul performance (qd8-f16) and FP16/FP32 support with softmax optimizations; extended RVV reductions and vectorized paths for f16/f32 operations; CI/build system upgrades to support broader architectures; bug fix improving GEMM f16-qb4w error-check reliability; and targeted code/script cleanups to improve maintainability and review efficiency. These efforts advance hardware-accelerated performance, robustness, and release readiness on RISC-V platforms.
February 2026
1 Commits
Feb 1, 2026February 2026 monthly summary for google/XNNPACK: Prioritized correctness and stability in floating-point vectorized paths. Delivered a bug fix ensuring floating-point type correctness by refactoring element tile calculations to consistently use the correct FP type. This fixes misconfiguration risks in FP paths across vectorized kernels, improving cross-platform reliability and reducing future debugging effort. No new product features this month; main value lies in robustness, maintainability, and long-term performance stability across FP workloads.
1 Commits
Feb 1, 2026February 2026 monthly summary for google/XNNPACK: Prioritized correctness and stability in floating-point vectorized paths. Delivered a bug fix ensuring floating-point type correctness by refactoring element tile calculations to consistently use the correct FP type. This fixes misconfiguration risks in FP paths across vectorized kernels, improving cross-platform reliability and reducing future debugging effort. No new product features this month; main value lies in robustness, maintainability, and long-term performance stability across FP workloads.
February 2026
January 2026
6 Commits • 1 Features
Jan 1, 2026Concise monthly summary for 2026-01 focusing on performance, portability, and correctness improvements in google/XNNPACK. Key RVV-related features delivered, critical floating-point robustness fixes, and configuration improvements for f16 unavailability. These changes expand hardware portability, enhance numerical accuracy, and strengthen test coverage, delivering measurable business value for performance-critical math kernels.
January 2026
6 Commits • 1 Features
Jan 1, 2026Concise monthly summary for 2026-01 focusing on performance, portability, and correctness improvements in google/XNNPACK. Key RVV-related features delivered, critical floating-point robustness fixes, and configuration improvements for f16 unavailability. These changes expand hardware portability, enhance numerical accuracy, and strengthen test coverage, delivering measurable business value for performance-critical math kernels.
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 monthly summary for google/XNNPACK. Delivered RISC-V RVV Quantized GEMM Kernel: Microkernel Support and Correctness Fix. Implemented RVV microkernels for quantized GEMM across multiple matrix sizes, with spill-free optimization to prevent register file spills. Also fixed correctness by reordering storage of intermediate results when MR < max rows to ensure correct data placement. Commits include 9c871c5c077f8b5799782be0888fd2db4d9494b4 and 84726d6ac67a0319b4cec5987308cf99be6a03cc. This work enhances performance and portability on RVV-enabled devices and improves correctness in edge-case MR scenarios.
2 Commits • 1 Features
May 1, 2025May 2025 monthly summary for google/XNNPACK. Delivered RISC-V RVV Quantized GEMM Kernel: Microkernel Support and Correctness Fix. Implemented RVV microkernels for quantized GEMM across multiple matrix sizes, with spill-free optimization to prevent register file spills. Also fixed correctness by reordering storage of intermediate results when MR < max rows to ensure correct data placement. Commits include 9c871c5c077f8b5799782be0888fd2db4d9494b4 and 84726d6ac67a0319b4cec5987308cf99be6a03cc. This work enhances performance and portability on RVV-enabled devices and improves correctness in edge-case MR scenarios.
May 2025
March 2025
11 Commits • 1 Features
Mar 1, 2025March 2025 performance summary for google/XNNPACK focused on expanding high-performance RVV (RISC-V Vector) support for quantized 8-bit kernels. Delivered a consolidated RVV path across qd8/qs8/qu8 GEMM/IGEMM, with associated test/benchmark regeneration, build/config updates, and targeted stability fixes. This work improves throughput on RVV-enabled hardware for quantized workloads while expanding maintainability and validation coverage.
March 2025
11 Commits • 1 Features
Mar 1, 2025March 2025 performance summary for google/XNNPACK focused on expanding high-performance RVV (RISC-V Vector) support for quantized 8-bit kernels. Delivered a consolidated RVV path across qd8/qs8/qu8 GEMM/IGEMM, with associated test/benchmark regeneration, build/config updates, and targeted stability fixes. This work improves throughput on RVV-enabled hardware for quantized workloads while expanding maintainability and validation coverage.
February 2025
3 Commits • 2 Features
Feb 1, 2025Concise monthly summary for 2025-02 focusing on delivered features, bug fixes, impact, and skills demonstrated for the google/XNNPACK repository.
3 Commits • 2 Features
Feb 1, 2025Concise monthly summary for 2025-02 focusing on delivered features, bug fixes, impact, and skills demonstrated for the google/XNNPACK repository.
February 2025
January 2025
5 Commits • 1 Features
Jan 1, 2025Month: 2025-01 | Repository: google/XNNPACK. This period focused on advancing RVV-accelerated quantized kernels and tightening benchmarking reliability. Key features delivered include QU8 depthwise convolution support on RVV with refactoring of quantized 8-bit depthwise convolutions for improved efficiency and correctness. Major bugs fixed include quantization correctness in RVV qs8-igemm (proper zero points and scaling), and a buffer-size calculation fix in GEMM benchmarking to prevent heap corruption; additional test code cleanup to improve readability. Benchmark configuration improvements included aligning MR tile size for the rvv qc8w-gemm microkernel benchmark to the expected tile size. Overall impact includes improved runtime reliability and performance of RVV-quantized kernels, more accurate benchmarks, and better maintainability. Technologies/skills demonstrated: C++ kernel development, quantization math, RVV vector extensions, benchmarking and validation, code quality and test hygiene.
January 2025
5 Commits • 1 Features
Jan 1, 2025Month: 2025-01 | Repository: google/XNNPACK. This period focused on advancing RVV-accelerated quantized kernels and tightening benchmarking reliability. Key features delivered include QU8 depthwise convolution support on RVV with refactoring of quantized 8-bit depthwise convolutions for improved efficiency and correctness. Major bugs fixed include quantization correctness in RVV qs8-igemm (proper zero points and scaling), and a buffer-size calculation fix in GEMM benchmarking to prevent heap corruption; additional test code cleanup to improve readability. Benchmark configuration improvements included aligning MR tile size for the rvv qc8w-gemm microkernel benchmark to the expected tile size. Overall impact includes improved runtime reliability and performance of RVV-quantized kernels, more accurate benchmarks, and better maintainability. Technologies/skills demonstrated: C++ kernel development, quantization math, RVV vector extensions, benchmarking and validation, code quality and test hygiene.
December 2024
2 Commits • 1 Features
Dec 1, 2024Month: 2024-12. Performance summary for google/XNNPACK focusing on quantized kernels with RVV optimization. Key features delivered: - RVV-accelerated quantized kernels for qs8-dwconv and QS8 GEMM/IGEMM, including kernel implementations, build/config updates, and benchmarks for RVV-enabled execution. Major bugs fixed: - No explicit bug fixes reported for this scope in December. Overall impact and accomplishments: - Enabled faster inference and improved energy efficiency on RVV-capable devices by adding RVV-accelerated paths for 8-bit quantized operations. - Expanded hardware support and performance visibility for quantized workloads, supporting edge/mobile deployment scenarios with improved throughput. - Strengthened build/configuration to enable and benchmark the RVV path, improving maintainability and optimization transparency. Technologies/skills demonstrated: - RISC-V Vector (RVV) optimization and kernel design for 8-bit quantized operations (qs8-dwconv, QS8 GEMM/IGEMM). - Depthwise convolution and GEMM/IGEMM kernel engineering, with build system integration and performance benchmarking. - Performance-oriented development focused on business value: speedups, energy efficiency, and hardware portability. Commit highlights: - ba490bbb0078f207011e264773b1d7cb7dde29dd: add qs8-dwconv support for rvv - b75f93fd702dc427e2dac18bb9be495589b9a6c: support qs8 gemm/igemm kernels for rvv
2 Commits • 1 Features
Dec 1, 2024Month: 2024-12. Performance summary for google/XNNPACK focusing on quantized kernels with RVV optimization. Key features delivered: - RVV-accelerated quantized kernels for qs8-dwconv and QS8 GEMM/IGEMM, including kernel implementations, build/config updates, and benchmarks for RVV-enabled execution. Major bugs fixed: - No explicit bug fixes reported for this scope in December. Overall impact and accomplishments: - Enabled faster inference and improved energy efficiency on RVV-capable devices by adding RVV-accelerated paths for 8-bit quantized operations. - Expanded hardware support and performance visibility for quantized workloads, supporting edge/mobile deployment scenarios with improved throughput. - Strengthened build/configuration to enable and benchmark the RVV path, improving maintainability and optimization transparency. Technologies/skills demonstrated: - RISC-V Vector (RVV) optimization and kernel design for 8-bit quantized operations (qs8-dwconv, QS8 GEMM/IGEMM). - Depthwise convolution and GEMM/IGEMM kernel engineering, with build system integration and performance benchmarking. - Performance-oriented development focused on business value: speedups, energy efficiency, and hardware portability. Commit highlights: - ba490bbb0078f207011e264773b1d7cb7dde29dd: add qs8-dwconv support for rvv - b75f93fd702dc427e2dac18bb9be495589b9a6c: support qs8 gemm/igemm kernels for rvv
December 2024
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Quality Metrics
Correctness98.4%
Maintainability91.8%
Architecture95.6%
Performance97.0%
AI Usage20.4%
Skills & Technologies
Programming Languages
BzlCC++CMakeCMakeScriptDockerfilePythonShellStarlarkYAML
Technical Skills
AssemblyAssembly LanguageAssembly languageBazelBenchmarkingBuild SystemBuild System ConfigurationCC ProgrammingC programmingC++C++ developmentC/C++C/C++ DevelopmentCMake
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