google-ai-edge/LiteRT
Mar 2026 – Jun 2026
3 Months active
Languages Used
C++
Technical Skills
C++ developmentmemory managementstream processingNumerical computingTensor manipulationGraph optimization
Yingying Ma
PROFILE
Yingying Ma
Worked on the google-ai-edge/LiteRT repository, delivering three core features over three months focused on C++ development, graph optimization, and memory management. Enhanced OpenVINO model export and import by implementing custom stream buffers and zero-copy memory handling, enabling reliable processing of large models while reducing memory pressure. Improved automated testing by adding robust output validation for boolean tensors, ensuring accurate numerical comparisons and eliminating misleading results. Developed a split-attention fusion optimization for Intel NPU, introducing a configurable pass that fuses split-attention into a single operation to improve inference efficiency. Demonstrated depth in numerical computing, tensor manipulation, and NPU optimization.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
3
Lines of code
872
Activity Months3
Your Network
2386 peopleSame Organization
@intel.com2215
Work History
June 2026
1 Commits • 1 Features
Jun 1, 2026June 2026 monthly summary for google-ai-edge/LiteRT. Delivered a split-attention fusion optimization for Intel NPU by introducing the FuseSplitAttentionToSDPA pass. This optimization fuses Gemma-style split-attention into a single ov::op::v13::ScaledDotProductAttention, enabling the NCE attention path on Intel OpenVINO. The feature is configurable via the fuse_split_attention_to_sdpa flag on IntelOpenVinoOptions and is disabled by default to avoid unintended changes; can be enabled with the config key. This work improves inference efficiency and throughput on Intel NPUs, contributing to lower latency and reduced energy per inference for edge deployments.
1 Commits • 1 Features
Jun 1, 2026June 2026 monthly summary for google-ai-edge/LiteRT. Delivered a split-attention fusion optimization for Intel NPU by introducing the FuseSplitAttentionToSDPA pass. This optimization fuses Gemma-style split-attention into a single ov::op::v13::ScaledDotProductAttention, enabling the NCE attention path on Intel OpenVINO. The feature is configurable via the fuse_split_attention_to_sdpa flag on IntelOpenVinoOptions and is disabled by default to avoid unintended changes; can be enabled with the config key. This work improves inference efficiency and throughput on Intel NPUs, contributing to lower latency and reduced energy per inference for edge deployments.
June 2026
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for google-ai-edge/LiteRT focused on delivering robust output validation for boolean tensors and strengthening automated testing reliability.
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for google-ai-edge/LiteRT focused on delivering robust output validation for boolean tensors and strengthening automated testing reliability.
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%
Architecture93.4%
Performance93.4%
AI Usage33.4%
Skills & Technologies
Programming Languages
C++
Technical Skills
C++ developmentGraph optimizationMachine learningNPU optimizationNumerical computingTensor manipulationmemory managementstream processing
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