CodeLinaro/onnxruntime
Jan 2026 – Feb 2026
2 Months active
Languages Used
C++
Technical Skills
C++ programmingGEMM operationsmultithreadingperformance optimizationquantizationC++ development
Melike Kaptan
PROFILE
Melike Kaptan
Melike Kaptan contributed to the CodeLinaro/onnxruntime repository by engineering two performance-focused features over a two-month period. She implemented multithreading for dynamic quantized GEMM operations using C++ and KleidiAI, optimizing memory usage and enabling scalable inference throughput across multi-core systems. In a separate effort, she enhanced the GridSample bilinear interpolation operator by precomputing neighbor indices and interpolation weights, and introduced ARM NEON vectorization to accelerate performance on ARM CPUs. Her work demonstrated a deep understanding of algorithm design, parallel programming, and vectorization, resulting in measurable runtime improvements and laying a foundation for further optimization within ONNX Runtime.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
1,300
Activity Months2
Your Network
589 peopleSame Organization
@arm.com524
Work History
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for CodeLinaro/onnxruntime. Primary focus: performance optimization of the GridSample bilinear interpolation operator. Delivered a plan-based optimization that precomputes neighbor indices and interpolation weights and reused them across channels, reducing per-output-pixel work. Introduced optional ARM NEON vectorization to boost throughput on ARM CPUs without impacting API. Demonstrated measurable runtime improvements in common workloads, with both single-threaded and multi-threaded configurations, contributing to lower latency in CPU-bound inference such as computer vision pipelines. No separate bug fixes were logged this month; changes are backward-compatible with existing ONNX Runtime workflows and emphasize efficiency, throughput, and scalability.
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for CodeLinaro/onnxruntime. Primary focus: performance optimization of the GridSample bilinear interpolation operator. Delivered a plan-based optimization that precomputes neighbor indices and interpolation weights and reused them across channels, reducing per-output-pixel work. Introduced optional ARM NEON vectorization to boost throughput on ARM CPUs without impacting API. Demonstrated measurable runtime improvements in common workloads, with both single-threaded and multi-threaded configurations, contributing to lower latency in CPU-bound inference such as computer vision pipelines. No separate bug fixes were logged this month; changes are backward-compatible with existing ONNX Runtime workflows and emphasize efficiency, throughput, and scalability.
February 2026
January 2026
1 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for CodeLinaro/onnxruntime. Focused on delivering a performance-oriented feature: enabling multithreading for dynamic quantized GEMM via KleidiAI, aimed at boosting inference throughput and optimizing memory usage in the qgemm_kleidi path. No major bug fixes recorded this month; the work centers on performance enhancement with clear traceability to issue #26301. This work lays groundwork for further dynamic quantization optimizations and scalability across cores. Technologies: C++, multithreading, dynamic quantization, memory optimization, KleidiAI integration, Git-based workflow.
January 2026
1 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for CodeLinaro/onnxruntime. Focused on delivering a performance-oriented feature: enabling multithreading for dynamic quantized GEMM via KleidiAI, aimed at boosting inference throughput and optimizing memory usage in the qgemm_kleidi path. No major bug fixes recorded this month; the work centers on performance enhancement with clear traceability to issue #26301. This work lays groundwork for further dynamic quantization optimizations and scalability across cores. Technologies: C++, multithreading, dynamic quantization, memory optimization, KleidiAI integration, Git-based workflow.
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance100.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
C++
Technical Skills
C++ developmentC++ programmingGEMM operationsalgorithm designmultithreadingparallel programmingperformance optimizationquantizationvectorization
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