CodeLinaro/onnxruntime
Apr 2025 – Jan 2026
7 Months active
Languages Used
C++
Technical Skills
C++Machine LearningTensor Processingbackend developmentunit testingalgorithm optimization
Ashwath Shankarnarayan
PROFILE
Ashwath Shankarnarayan
Ashwin Shanbhag contributed to the CodeLinaro/onnxruntime repository by developing and enhancing the QNN Execution Provider, focusing on expanding operator coverage and optimizing backend performance for machine learning workloads. He implemented features such as Int64 tensor support, operator fusion for high-dimensional tensors, and advanced quantization handling for convolution operations. Using C++ and ONNX Runtime, Ashwin designed operator builders and fusion patterns to enable efficient tensor manipulation, signal processing, and quantized inference. His work emphasized robust unit testing, detailed logging, and regression prevention, resulting in improved model compatibility, reduced CPU usage, and more reliable inference on QNN-capable hardware platforms.
Overall Statistics
Feature vs Bugs
82%Features
Repository Contributions
13Total
Bugs
2
Commits
13
Features
9
Lines of code
3,732
Activity Months7
Your Network
206 people
Work History
January 2026
2 Commits • 2 Features
Jan 1, 2026January 2026: Key QNN EP enhancements in CodeLinaro/onnxruntime focusing on quantized inference robustness and debuggability. Delivered two major items: 1) Static Bias Tensor support for Conv with requantization to handle mismatched quantization encodings, 2) Detailed graph IO/Initializer logging for the QNN Execution Provider. No explicit bugs fixed were recorded this month; improvements in quantization handling and observability reduce debugging time and improve inference reliability. Technologies demonstrated include quantization, Conv operations, QNN EP, and logging/memory management.
2 Commits • 2 Features
Jan 1, 2026January 2026: Key QNN EP enhancements in CodeLinaro/onnxruntime focusing on quantized inference robustness and debuggability. Delivered two major items: 1) Static Bias Tensor support for Conv with requantization to handle mismatched quantization encodings, 2) Detailed graph IO/Initializer logging for the QNN Execution Provider. No explicit bugs fixed were recorded this month; improvements in quantization handling and observability reduce debugging time and improve inference reliability. Technologies demonstrated include quantization, Conv operations, QNN EP, and logging/memory management.
January 2026
October 2025
1 Commits • 1 Features
Oct 1, 20252025-10 monthly summary for CodeLinaro/onnxruntime: Implemented QNN Execution Provider 6D Tensor Fusion for Reshape -> Transpose -> Reshape, enabling efficient handling of 6D tensors by mapping to a rank-5 representation. Updated QNN EP node group registration and added a dedicated fusion class to manage this pattern. This work lays groundwork for improved performance on high-dimensional models and contributes to the QNN EP optimization objectives.
October 2025
1 Commits • 1 Features
Oct 1, 20252025-10 monthly summary for CodeLinaro/onnxruntime: Implemented QNN Execution Provider 6D Tensor Fusion for Reshape -> Transpose -> Reshape, enabling efficient handling of 6D tensors by mapping to a rank-5 representation. Updated QNN EP node group registration and added a dedicated fusion class to manage this pattern. This work lays groundwork for improved performance on high-dimensional models and contributes to the QNN EP optimization objectives.
September 2025
3 Commits • 2 Features
Sep 1, 2025September 2025 Monthly Summary for CodeLinaro/onnxruntime (QNN EP) Key features delivered and major fixes: - QNN STFT support: Implemented Short-Time Fourier Transform operation in the QNN execution provider, including a dedicated op builder and comprehensive unit tests, enabling broader model support for real-time audio and signal processing workloads. - QNN RandomUniformLike support: Added RandomUniformLike operation with op builder and unit tests, expanding random tensor generation capabilities in QNN EP and improving parity with ONNX Runtime operators. - QNN GatherND int64 unique name fix and test: Resolved a bug where UniqueName was generated more than once for int64 tensor casts in GatherND; added a unit test for Gather with int64 indices to prevent regressions. Impact and outcomes: - Broader operator coverage in QNN EP leads to more models running efficiently on constrained hardware, delivering tangible performance and inference-capability improvements for customers relying on QNN EP. - Strengthened test suite and regression resistance, increasing reliability and reducing risk of future GatherND-related regressions. Technologies and skills demonstrated: - Deepened expertise with QNN EP integration patterns, op builder design, and unit-test driven validation. - Proficiency in extending ONNX Runtime backends, with clear commit hygiene and traceability to feature/bug fixes. - Emphasis on business value through asset preservation (regression tests) and expanded hardware-optimized execution paths. Top 3-5 achievements: 1) Added STFT support in QNN EP with op builder and unit tests. commits: d3a916dbc43363a1f3e0f986f77bca89cdc79831 2) Added RandomUniformLike support in QNN EP with op builder and unit tests. commits: 8c5d2b58ca9e62d61e861d12d4fcdd98740d9951 3) Fixed GatherND int64 unique name handling and added int64 index test for Gather operation. commits: 9aa3423de4c858531a085d41ea56b8b6d84d722a
3 Commits • 2 Features
Sep 1, 2025September 2025 Monthly Summary for CodeLinaro/onnxruntime (QNN EP) Key features delivered and major fixes: - QNN STFT support: Implemented Short-Time Fourier Transform operation in the QNN execution provider, including a dedicated op builder and comprehensive unit tests, enabling broader model support for real-time audio and signal processing workloads. - QNN RandomUniformLike support: Added RandomUniformLike operation with op builder and unit tests, expanding random tensor generation capabilities in QNN EP and improving parity with ONNX Runtime operators. - QNN GatherND int64 unique name fix and test: Resolved a bug where UniqueName was generated more than once for int64 tensor casts in GatherND; added a unit test for Gather with int64 indices to prevent regressions. Impact and outcomes: - Broader operator coverage in QNN EP leads to more models running efficiently on constrained hardware, delivering tangible performance and inference-capability improvements for customers relying on QNN EP. - Strengthened test suite and regression resistance, increasing reliability and reducing risk of future GatherND-related regressions. Technologies and skills demonstrated: - Deepened expertise with QNN EP integration patterns, op builder design, and unit-test driven validation. - Proficiency in extending ONNX Runtime backends, with clear commit hygiene and traceability to feature/bug fixes. - Emphasis on business value through asset preservation (regression tests) and expanded hardware-optimized execution paths. Top 3-5 achievements: 1) Added STFT support in QNN EP with op builder and unit tests. commits: d3a916dbc43363a1f3e0f986f77bca89cdc79831 2) Added RandomUniformLike support in QNN EP with op builder and unit tests. commits: 8c5d2b58ca9e62d61e861d12d4fcdd98740d9951 3) Fixed GatherND int64 unique name handling and added int64 index test for Gather operation. commits: 9aa3423de4c858531a085d41ea56b8b6d84d722a
September 2025
August 2025
1 Commits • 1 Features
Aug 1, 20252025-08 Monthly Summary for CodeLinaro/onnxruntime. Focused on delivering functionality that expands model expressiveness on hardware-accelerated paths while maintaining test coverage and code quality.
August 2025
1 Commits • 1 Features
Aug 1, 20252025-08 Monthly Summary for CodeLinaro/onnxruntime. Focused on delivering functionality that expands model expressiveness on hardware-accelerated paths while maintaining test coverage and code quality.
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025: Expanded QNN Execution Provider coverage for ONNX ops in CodeLinaro/onnxruntime. Delivered Reciprocal and Mean operator support by introducing operator builders that decompose the ONNX operations into existing QNN primitives (Reciprocal -> Div; Mean -> Add series followed by Div) with accompanying unit tests. No major bugs fixed this month. Impact: broader model compatibility on QNN backends, improved inference performance and maintainability through decomposition patterns. Technologies/skills demonstrated: ONNX, QNN EP architecture, operator builder pattern, unit test development, and CI-ready changes.
2 Commits • 1 Features
Jun 1, 2025June 2025: Expanded QNN Execution Provider coverage for ONNX ops in CodeLinaro/onnxruntime. Delivered Reciprocal and Mean operator support by introducing operator builders that decompose the ONNX operations into existing QNN primitives (Reciprocal -> Div; Mean -> Add series followed by Div) with accompanying unit tests. No major bugs fixed this month. Impact: broader model compatibility on QNN backends, improved inference performance and maintainability through decomposition patterns. Technologies/skills demonstrated: ONNX, QNN EP architecture, operator builder pattern, unit test development, and CI-ready changes.
June 2025
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 monthly achievements for CodeLinaro/onnxruntime focusing on QNN Execution Provider (QNN EP) improvements. Delivered key feature enabling rank-3 MaxPool support and resolved critical correctness issues, along with test coverage to prevent regressions.
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 monthly achievements for CodeLinaro/onnxruntime focusing on QNN Execution Provider (QNN EP) improvements. Delivered key feature enabling rank-3 MaxPool support and resolved critical correctness issues, along with test coverage to prevent regressions.
April 2025
2 Commits • 1 Features
Apr 1, 2025April 2025 performance review: Implemented major QNN Execution Provider enhancements in onnxruntime (CodeLinaro/onnxruntime). Delivered Int64 tensor support and ScatterND operation, with repository-wide improvements including unit tests and builder updates to enable broader model support, reduce CPU usage, and boost inference performance on QNN-capable devices.
2 Commits • 1 Features
Apr 1, 2025April 2025 performance review: Implemented major QNN Execution Provider enhancements in onnxruntime (CodeLinaro/onnxruntime). Delivered Int64 tensor support and ScatterND operation, with repository-wide improvements including unit tests and builder updates to enable broader model support, reduce CPU usage, and boost inference performance on QNN-capable devices.
April 2025
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Quality Metrics
Correctness94.6%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage29.2%
Skills & Technologies
Programming Languages
C++
Technical Skills
C++C++ developmentData SerializationDebuggingLoggingMachine LearningNeural NetworksONNXONNX RuntimeOperator FusionQNNQNN EPQNN execution providerSignal ProcessingTensor Manipulation
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