CodeLinaro/onnxruntime
Jan 2026 – Jan 2026
1 Month active
Languages Used
C++
Technical Skills
C++ONNXQNNbackend developmentmachine learningperformance optimization
tirupath-qti
PROFILE
Tirupath-qti
During January 2026, Tirupath contributed to the CodeLinaro/onnxruntime repository by enhancing the QNN execution provider to support additional operators and data types. He implemented RMSNorm, QuickGELU, and FusedMatMul operator support, as well as BFloat16 data type handling, all in C++. His work focused on improving model compatibility and performance for ONNX Runtime users leveraging QNN acceleration. Tirupath emphasized correctness and reliability by adding comprehensive unit tests, ensuring robust integration of new features. These enhancements expanded hardware support and reduced inference latency, enabling broader adoption of QNN-accelerated models and lowering runtime costs for customers in machine learning applications.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
1
Lines of code
2,253
Activity Months1
Your Network
144 people
Work History
January 2026
4 Commits • 1 Features
Jan 1, 2026Monthly work summary for CodeLinaro/onnxruntime - 2026-01 Key focus: QNN Execution Provider enhancements driving model compatibility and performance, with added tests to ensure reliability. Business value centers on expanding hardware support, reducing inference latency, and enabling broader adoption of QNN-accelerated models. Overall context: The month was dedicated to implementing and validating major QNN EP feature upgrades, with an emphasis on correctness, test coverage, and performance-oriented changes that translate to lower runtime costs for customers using QNN with ONNX Runtime.
4 Commits • 1 Features
Jan 1, 2026Monthly work summary for CodeLinaro/onnxruntime - 2026-01 Key focus: QNN Execution Provider enhancements driving model compatibility and performance, with added tests to ensure reliability. Business value centers on expanding hardware support, reducing inference latency, and enabling broader adoption of QNN-accelerated models. Overall context: The month was dedicated to implementing and validating major QNN EP feature upgrades, with an emphasis on correctness, test coverage, and performance-oriented changes that translate to lower runtime costs for customers using QNN with ONNX Runtime.
January 2026
Activity
Loading activity data...
Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance85.0%
AI Usage35.0%
Skills & Technologies
Programming Languages
C++
Technical Skills
C++ONNXQNNbackend developmentmachine learningperformance optimizationquantizationunit testing
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline