google/XNNPACK
Aug 2025 – Feb 2026
4 Months active
Languages Used
CC++BazelCMakeYAMLBashPython
Technical Skills
embedded systemslow-level programmingmatrix multiplication algorithmsperformance optimizationARM architectureC programming
Vaisakh K V
PROFILE
Vaisakh K V
Vaisakh worked on performance-critical features and infrastructure for google/XNNPACK and CodeLinaro/onnxruntime, focusing on ARM and Qualcomm hardware acceleration. He developed and optimized matrix multiplication and convolution kernels, introducing SME and QMX support to improve inference throughput on edge devices. His work included implementing new microkernels in C and C++, enhancing build automation and cross-platform CI/CD with Bazel and CMake, and ensuring robust testing and open source compliance. By upgrading build and testing workflows across Linux, Windows, and macOS, Vaisakh enabled more reliable integration and validation, demonstrating depth in low-level programming, algorithm optimization, and hardware-specific performance engineering.
Overall Statistics
Feature vs Bugs
71%Features
Repository Contributions
14Total
Bugs
2
Commits
14
Features
5
Lines of code
3,148,896
Activity Months5
Your Network
306 peopleSame Organization
@qti.qualcomm.com185
Shared Repositories
121
Work History
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 — google/XNNPACK: Cross-Platform Build and Testing Infrastructure Upgrade. Merged latest master into sme1/pqs8-qc8w-gemm-igemm feature branch and introduced configuration files and build scripts to streamline Linux, Windows, and macOS builds with ARM and x86 support, improving reliability of cross-platform development and testing workflows. This upgrade reduces integration risk and accelerates feature validation across architectures.
1 Commits • 1 Features
Feb 1, 2026February 2026 — google/XNNPACK: Cross-Platform Build and Testing Infrastructure Upgrade. Merged latest master into sme1/pqs8-qc8w-gemm-igemm feature branch and introduced configuration files and build scripts to streamline Linux, Windows, and macOS builds with ARM and x86 support, improving reliability of cross-platform development and testing workflows. This upgrade reduces integration risk and accelerates feature validation across architectures.
February 2026
January 2026
1 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for CodeLinaro/onnxruntime: Delivered Qualcomm QMX kernel support in ONNX Runtime MLAS, enabling SGEMM, QGEMM, and Convolution to leverage QMX optimizations on Qualcomm hardware. This work expands MLAS hardware acceleration coverage and is expected to improve inference throughput on QC platforms. No major bugs reported this month. Overall impact: stronger performance and broader hardware support with a solid foundation for future QC optimizations. Technologies demonstrated: ML acceleration backends, kernel integration, cross-hardware optimization, and code delivery discipline.
January 2026
1 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for CodeLinaro/onnxruntime: Delivered Qualcomm QMX kernel support in ONNX Runtime MLAS, enabling SGEMM, QGEMM, and Convolution to leverage QMX optimizations on Qualcomm hardware. This work expands MLAS hardware acceleration coverage and is expected to improve inference throughput on QC platforms. No major bugs reported this month. Overall impact: stronger performance and broader hardware support with a solid foundation for future QC optimizations. Technologies demonstrated: ML acceleration backends, kernel integration, cross-hardware optimization, and code delivery discipline.
December 2025
2 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for google/XNNPACK focusing on performance optimizations in the convolution path. Implemented a pf32 igemm kernel added to the fingerprinting method and applied inline left-hand side packing only for convolution2d nodes, delivering faster FP convolution throughput while maintaining numerical accuracy. The changes improve efficiency of packed input data handling, contributing to lower latency and better energy efficiency on edge devices.
2 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for google/XNNPACK focusing on performance optimizations in the convolution path. Implemented a pf32 igemm kernel added to the fingerprinting method and applied inline left-hand side packing only for convolution2d nodes, delivering faster FP convolution throughput while maintaining numerical accuracy. The changes improve efficiency of packed input data handling, contributing to lower latency and better energy efficiency on edge devices.
December 2025
November 2025
9 Commits • 1 Features
Nov 1, 2025November 2025 monthly summary for google/XNNPACK focusing on ARM SME1 optimization and FP16 GEMM/IGEMM support, SME1 compatibility for Kleidiai, and licensing/compliance updates. Key work delivered includes new SME1-enabled GEMM microkernels, tests, and performance benchmarks with SME configuration updates, plus packaging and test automation improvements. Also delivered Kleidiai SME1 compatibility fixes and library version updates to pull the fixed matmul_clamp_f32_qai8dxp_qsi8cxp SME1 variant, and licensing/copyright compliance enhancements.
November 2025
9 Commits • 1 Features
Nov 1, 2025November 2025 monthly summary for google/XNNPACK focusing on ARM SME1 optimization and FP16 GEMM/IGEMM support, SME1 compatibility for Kleidiai, and licensing/compliance updates. Key work delivered includes new SME1-enabled GEMM microkernels, tests, and performance benchmarks with SME configuration updates, plus packaging and test automation improvements. Also delivered Kleidiai SME1 compatibility fixes and library version updates to pull the fixed matmul_clamp_f32_qai8dxp_qsi8cxp SME1 variant, and licensing/copyright compliance enhancements.
August 2025
1 Commits • 1 Features
Aug 1, 2025Monthly summary for 2025-08 focused on feature delivery and performance optimization for google/XNNPACK.
1 Commits • 1 Features
Aug 1, 2025Monthly summary for 2025-08 focused on feature delivery and performance optimization for google/XNNPACK.
August 2025
Activity
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Quality Metrics
Correctness95.8%
Maintainability87.0%
Architecture93.0%
Performance95.8%
AI Usage21.4%
Skills & Technologies
Programming Languages
BashBazelCC++CMakePythonYAML
Technical Skills
ARM architectureBazelC programmingC++ developmentC++ programmingCI/CDCMakeDependency ManagementLibrary Managementalgorithm designalgorithm optimizationbenchmarkingbuild automationbuild system configurationcross-platform development
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline
CodeLinaro/onnxruntime
Jan 2026 – Jan 2026
1 Month active
Languages Used
C++CMake
Technical Skills
C++ developmentCMakemachine learningperformance optimization