google/XNNPACK
Jan 2025 – Apr 2025
2 Months active
Languages Used
CCMakePython
Technical Skills
Assembly programmingC programmingDeep LearningEmbedded SystemsLow-level optimizationMachine Learning
Swami, Preksha
PROFILE
Swami, Preksha
Preksha Swami contributed to google/XNNPACK by developing and integrating low-precision PReLU microkernels for QS8 and QU8 data types, targeting both AVX2 and scalar execution paths. Using C and assembly programming, she implemented new kernel logic, updated build and generation scripts, and created comprehensive tests to ensure correctness and performance for quantized inference workloads. In a subsequent effort, she refactored the quantized ReLU path, removing unused variables and improving maintainability across AVX2 and scalar code. Her work enhanced data-type support, runtime efficiency, and code quality for quantized deep learning models, demonstrating depth in low-level optimization and SIMD programming.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
5,914
Activity Months2
Your Network
2002 people
Work History
April 2025
1 Commits • 1 Features
Apr 1, 2025Monthly summary for 2025-04: Focused on cleaning up the Quantized ReLU path in google/XNNPACK to reduce technical debt and stabilize SIMD-optimized code paths. The work refactored quantized integer operations across AVX2 and scalar paths, improving maintainability and potentially reducing binary size over time. This aligns with performance and reliability goals for quantized inference workloads.
1 Commits • 1 Features
Apr 1, 2025Monthly summary for 2025-04: Focused on cleaning up the Quantized ReLU path in google/XNNPACK to reduce technical debt and stabilize SIMD-optimized code paths. The work refactored quantized integer operations across AVX2 and scalar paths, improving maintainability and potentially reducing binary size over time. This aligns with performance and reliability goals for quantized inference workloads.
April 2025
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025: Deliverable-focused month for google/XNNPACK focused on expanding low-precision activation support through QS8/QU8 PReLU microkernels. Implemented AVX2 and scalar path microkernels with accompanying C sources and tests, integrated via build/generation script updates to streamline compilation and integration. This work broadens data-type coverage, enhances runtime performance for quantized models on modern CPUs, and improves test coverage for kernel correctness. Commit referenced: a6e9d9924f099ad3d83c09b65847573096c6f458.
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025: Deliverable-focused month for google/XNNPACK focused on expanding low-precision activation support through QS8/QU8 PReLU microkernels. Implemented AVX2 and scalar path microkernels with accompanying C sources and tests, integrated via build/generation script updates to streamline compilation and integration. This work broadens data-type coverage, enhances runtime performance for quantized models on modern CPUs, and improves test coverage for kernel correctness. Commit referenced: a6e9d9924f099ad3d83c09b65847573096c6f458.
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CCMakePython
Technical Skills
AssemblyAssembly programmingC programmingDeep LearningEmbedded SystemsLow-level optimizationMachine LearningPerformance OptimizationPerformance optimizationSIMD programming
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