
Shzhen developed end-to-end QNN optimization examples for transformer models in the microsoft/Olive repository, focusing on Table Transformer Detection and Sentence Transformer scenarios. By integrating the ONNX Runtime QNN execution provider, Shzhen enabled faster inference while maintaining model accuracy, providing production-ready benchmarks and comprehensive documentation to support customer evaluation. The work included preparing datasets, writing Python evaluation scripts, and updating documentation to reflect expected latency improvements. In the microsoft/windows-ai-studio-templates repository, Shzhen standardized asset metadata and model configuration using JSON, improving consistency and simplifying future changes. The contributions demonstrated depth in configuration management, data preparation, and model optimization workflows.

July 2025 monthly summary for microsoft/windows-ai-studio-templates: Delivered Asset Metadata Harmonization and Model Configuration Standardization, establishing consistent icon asset metadata and standardized model configuration data across the repository. This groundwork improves downstream reliability, simplifies asset management, and accelerates future configuration changes.
July 2025 monthly summary for microsoft/windows-ai-studio-templates: Delivered Asset Metadata Harmonization and Model Configuration Standardization, establishing consistent icon asset metadata and standardized model configuration data across the repository. This groundwork improves downstream reliability, simplifies asset management, and accelerates future configuration changes.
March 2025 Monthly Summary - microsoft/Olive - Key features delivered: QNN optimization examples for two user-facing scenarios added to Olive: 1) Table Transformer Detection using a smaller TableBank dataset, and 2) Sentence Transformer models. The examples include end-to-end assets (datasets and preparation scripts) and evaluation/demo scripts, enabling quick customer evaluation of QNN-based acceleration. - Major bugs fixed: none reported this month. Primary focus was feature delivery and documentation. - Documentation and knowledge transfer: Documentation updated to reflect the new examples and the expected latency improvements while preserving accuracy. - Commits and code changes: Implemented two commits to add the new examples: • 54d23fc099066bb4af73f3987e18a15a2bd6efb1 - Add Table Transformer Detection QNN example (#1661) • f57bb8c4ce54d68ab8265c072700b3c137186d7f - Add Sentence Transformer QNN example (#1694) - Overall impact and accomplishments: Demonstrated practical integration of ONNX Runtime QNN execution provider within Olive pipelines, enabling faster inferences for transformer models. Provided production-ready benchmarks and demos to accelerate customer evaluation and adoption. - Technologies/skills demonstrated: ONNX Runtime QNN execution provider, performance optimization, dataset preparation, benchmarking/evaluation scripts, end-to-end demo assets, and comprehensive documentation.
March 2025 Monthly Summary - microsoft/Olive - Key features delivered: QNN optimization examples for two user-facing scenarios added to Olive: 1) Table Transformer Detection using a smaller TableBank dataset, and 2) Sentence Transformer models. The examples include end-to-end assets (datasets and preparation scripts) and evaluation/demo scripts, enabling quick customer evaluation of QNN-based acceleration. - Major bugs fixed: none reported this month. Primary focus was feature delivery and documentation. - Documentation and knowledge transfer: Documentation updated to reflect the new examples and the expected latency improvements while preserving accuracy. - Commits and code changes: Implemented two commits to add the new examples: • 54d23fc099066bb4af73f3987e18a15a2bd6efb1 - Add Table Transformer Detection QNN example (#1661) • f57bb8c4ce54d68ab8265c072700b3c137186d7f - Add Sentence Transformer QNN example (#1694) - Overall impact and accomplishments: Demonstrated practical integration of ONNX Runtime QNN execution provider within Olive pipelines, enabling faster inferences for transformer models. Provided production-ready benchmarks and demos to accelerate customer evaluation and adoption. - Technologies/skills demonstrated: ONNX Runtime QNN execution provider, performance optimization, dataset preparation, benchmarking/evaluation scripts, end-to-end demo assets, and comprehensive documentation.
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