google-ai-edge/LiteRT
Mar 2026 – Mar 2026
1 Month active
Languages Used
C++
Technical Skills
C++ developmentmemory managementstream processing
yingyingma
PROFILE
Yingyingma
Yingying Ma enhanced the google-ai-edge/LiteRT repository by developing memory-efficient export and import capabilities for OpenVINO models in C++. She addressed large-model limitations by implementing custom std::streambufs, enabling reliable handling of substantial model bytecodes while reducing memory pressure. Her approach included a zero-copy import path that wraps pre-allocated memory, eliminating unnecessary data copies and preventing out-of-memory errors during large model loads. On export, she bypassed the 32-bit std::streamsize limit in string streams, resolving failures for very large models. This work demonstrated deep proficiency in C++ memory management, stream processing, and integration with OpenVINO deployment workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
143
Activity Months1
Your Network
2162 peopleSame Organization
@intel.com2011
Work History
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026: Delivered memory-efficient OpenVINO model export/import enhancements in LiteRT. Implemented custom std::streambufs to address large-model limitations, enabling reliable handling of big model bytecodes and reducing memory pressure. Implemented a zero-copy import path by wrapping pre-allocated memory, eliminating unnecessary copies and preventing OOM during large model loads. On the export side, bypassed the 32-bit std::streamsize limit in string streams, fixing export failures for very large models. These changes improve stability, scalability, and deployment reliability for enterprise OpenVINO workflows integrated with LiteRT. Demonstrated strong proficiency with C++ stream internals, memory management, and OpenVINO integration."
1 Commits • 1 Features
Mar 1, 2026March 2026: Delivered memory-efficient OpenVINO model export/import enhancements in LiteRT. Implemented custom std::streambufs to address large-model limitations, enabling reliable handling of big model bytecodes and reducing memory pressure. Implemented a zero-copy import path by wrapping pre-allocated memory, eliminating unnecessary copies and preventing OOM during large model loads. On the export side, bypassed the 32-bit std::streamsize limit in string streams, fixing export failures for very large models. These changes improve stability, scalability, and deployment reliability for enterprise OpenVINO workflows integrated with LiteRT. Demonstrated strong proficiency with C++ stream internals, memory management, and OpenVINO integration."
March 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++
Technical Skills
C++ developmentmemory managementstream processing
Repositories Contributed To
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