Intel-tensorflow/tensorflow
Jun 2025 – Sep 2025
2 Months active
Languages Used
C++MLIR
Technical Skills
C++MLIRTensorFlowmachine learningcompiler design
Jing Pu
PROFILE
Jing Pu
Jingpu contributed to Intel-tensorflow/tensorflow and ROCm/tensorflow-upstream by developing and refining core features for deep learning workflows. Over three months, Jingpu built a Unified 2D Convolution-Bias-Activation operator in C++ and MLIR, enabling fused computation for convolution, bias addition, and activation, which improved performance and model flexibility. Jingpu also enhanced reliability by introducing a targeted guard to prevent unsupported 8-bit integer convolutions during MHLO to TensorFlow transformations, reducing runtime errors. Additionally, Jingpu simplified the GatherNd implementation in ROCm/tensorflow-upstream by reverting experimental features, aligning with upstream TensorFlow and improving maintainability. The work demonstrated strong compiler and machine learning expertise.
Overall Statistics
Feature vs Bugs
33%Features
Repository Contributions
3Total
Bugs
2
Commits
3
Features
1
Lines of code
198
Activity Months3
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4833 peopleSame Organization
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Shared Repositories
448
Work History
December 2025
1 Commits
Dec 1, 2025December 2025: Focused on stability and maintainability of the ROCm/tensorflow-upstream GatherNd path. Reverted the experimental bad_indices_policy feature in GatherNd, removing handling of bad indices and simplifying the implementation. The revert ensures compatibility with upstream TensorFlow behavior, reduces maintenance burden, and improves reliability for downstream users. Commit details: 2cc7d2ac894e6389568b0f358966963ca45808c1.
1 Commits
Dec 1, 2025December 2025: Focused on stability and maintainability of the ROCm/tensorflow-upstream GatherNd path. Reverted the experimental bad_indices_policy feature in GatherNd, removing handling of bad indices and simplifying the implementation. The revert ensures compatibility with upstream TensorFlow behavior, reduces maintenance burden, and improves reliability for downstream users. Commit details: 2cc7d2ac894e6389568b0f358966963ca45808c1.
December 2025
September 2025
1 Commits
Sep 1, 2025September 2025 monthly summary focusing on stabilizing 8-bit integer input handling in MHLO→TensorFlow transformations within Intel-tensorflow/tensorflow. Delivered a targeted guard that disables the MHLO→TF ConvOp conversion for Int8 inputs, preventing runtime errors and improving reliability for int8 inference workflows.
September 2025
1 Commits
Sep 1, 2025September 2025 monthly summary focusing on stabilizing 8-bit integer input handling in MHLO→TensorFlow transformations within Intel-tensorflow/tensorflow. Delivered a targeted guard that disables the MHLO→TF ConvOp conversion for Int8 inputs, preventing runtime errors and improving reliability for int8 inference workflows.
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary focused on feature delivery in Intel-tensorflow/tensorflow: introduced the Unified 2D Convolution-Bias-Activation operator, enabling fused computation for convolution, bias addition, and activation. This feature enhances performance and design flexibility for CNN models in production pipelines.
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary focused on feature delivery in Intel-tensorflow/tensorflow: introduced the Unified 2D Convolution-Bias-Activation operator, enabling fused computation for convolution, bias addition, and activation. This feature enhances performance and design flexibility for CNN models in production pipelines.
June 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability86.6%
Architecture93.4%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++MLIRPython
Technical Skills
C++C++ DevelopmentMLIRMachine LearningPython DevelopmentTensorFlowcompiler designmachine learning
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline
ROCm/tensorflow-upstream
Dec 2025 – Dec 2025
1 Month active
Languages Used
C++Python
Technical Skills
C++ DevelopmentMachine LearningPython DevelopmentTensorFlow