Samsung/ONE
Nov 2024 – Oct 2025
6 Months active
Languages Used
C++CMakeMakefileC
Technical Skills
Compiler DevelopmentEmbedded SystemsQuantizationAPI DevelopmentC++CMake
Evgenii Maltsev
PROFILE
Evgenii Maltsev
Eugene Maltsev developed and enhanced quantization, kernel, and runtime features for the Samsung/ONE repository, focusing on embedded systems and model optimization. He implemented quantization readiness management and auto-compilation support, enabling deterministic quantization and streamlined deployment to target hardware. Using C++ and CMake, Eugene extended onert-micro kernel support for TizenRT, added int8 data type support for Split and Transpose kernels, and improved kernel robustness through status propagation and input bounds checks. His work included comprehensive test coverage and targeted bug fixes, ensuring reliable production inference. The depth of his contributions reflects strong expertise in compiler and kernel development.
Overall Statistics
Feature vs Bugs
56%Features
Repository Contributions
12Total
Bugs
4
Commits
12
Features
5
Lines of code
1,969
Activity Months6
Your Network
196 people
Work History
October 2025
1 Commits
Oct 1, 2025October 2025 monthly summary for Samsung/ONE: Stabilized ReduceMax kernel verification by fixing axis iteration logic and expanding test coverage. Delivered a targeted bug fix and enabled validation tests to ensure correctness, reducing regression risk and increasing confidence in kernel behavior for production deployments.
1 Commits
Oct 1, 2025October 2025 monthly summary for Samsung/ONE: Stabilized ReduceMax kernel verification by fixing axis iteration logic and expanding test coverage. Delivered a targeted bug fix and enabled validation tests to ensure correctness, reducing regression risk and increasing confidence in kernel behavior for production deployments.
October 2025
September 2025
3 Commits • 1 Features
Sep 1, 2025In September 2025, delivered robustness enhancements and capability expansions for Samsung/ONE's ONERT-micro kernels, aligning with reliability and performance goals for production inference workloads.
September 2025
3 Commits • 1 Features
Sep 1, 2025In September 2025, delivered robustness enhancements and capability expansions for Samsung/ONE's ONERT-micro kernels, aligning with reliability and performance goals for production inference workloads.
August 2025
3 Commits • 1 Features
Aug 1, 2025August 2025: Progress on onert-micro for Samsung/ONE with a focus on quantized inference readiness and kernel robustness. Implemented int8 support for Split and Transpose kernels with tests/models, and fixed OMRuntimeKernel inplace handling for constant inputs, boosting reliability and performance of quantized workloads.
3 Commits • 1 Features
Aug 1, 2025August 2025: Progress on onert-micro for Samsung/ONE with a focus on quantized inference readiness and kernel robustness. Implemented int8 support for Split and Transpose kernels with tests/models, and fixed OMRuntimeKernel inplace handling for constant inputs, boosting reliability and performance of quantized workloads.
August 2025
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary focusing on key accomplishments for Samsung/ONE. Key feature delivered: extend onert-micro kernel support for TizenRT by adding new C++ sources to both the execute and import kernel Makefiles, enabling additional onert-micro operations within the TizenRT integration. This work improves modularity and future extensibility of the onert-micro framework on TizenRT. Commit associated with the change: 413accc39b8ff90a420d1b6f96fb3bc0599c0ddd.
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary focusing on key accomplishments for Samsung/ONE. Key feature delivered: extend onert-micro kernel support for TizenRT by adding new C++ sources to both the execute and import kernel Makefiles, enabling additional onert-micro operations within the TizenRT integration. This work improves modularity and future extensibility of the onert-micro framework on TizenRT. Commit associated with the change: 413accc39b8ff90a420d1b6f96fb3bc0599c0ddd.
December 2024
3 Commits • 1 Features
Dec 1, 2024December 2024: Delivered foundational Auto-compilation support for NNFW/odc in Samsung/ONE, enabling runtime auto-quantization and model compilation with min-max stats, plus enhancements to Execution buffer accessors and robust test coverage. This accelerates deployment to target hardware, reduces manual tuning, and improves reliability across the NNFW/odc pipeline.
3 Commits • 1 Features
Dec 1, 2024December 2024: Delivered foundational Auto-compilation support for NNFW/odc in Samsung/ONE, enabling runtime auto-quantization and model compilation with min-max stats, plus enhancements to Execution buffer accessors and robust test coverage. This accelerates deployment to target hardware, reduces manual tuning, and improves reliability across the NNFW/odc pipeline.
December 2024
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary focusing on quantization readiness management delivered for Samsung/ONE. Implemented Quantization Readiness Management to control auto-compilation timing based on a minimum number of minmax records, including threshold configuration, readiness checks, and the ability to delete the minmax file. This feature enhances deterministic quantization and reduces risk in the production build pipeline. No explicit bugs fixed were logged in this period; the main focus was feature delivery and stabilization of the quantization workflow.
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary focusing on quantization readiness management delivered for Samsung/ONE. Implemented Quantization Readiness Management to control auto-compilation timing based on a minimum number of minmax records, including threshold configuration, readiness checks, and the ability to delete the minmax file. This feature enhances deterministic quantization and reduces risk in the production build pipeline. No explicit bugs fixed were logged in this period; the main focus was feature delivery and stabilization of the quantization workflow.
Activity
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Quality Metrics
Correctness90.0%
Maintainability86.6%
Architecture83.4%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CC++CMakeMakefile
Technical Skills
API DevelopmentBuild SystemsC++CMakeCompiler DevelopmentEmbedded SystemsEmbedded Systems DevelopmentKernel DevelopmentMachine Learning FrameworksModel OptimizationQuantizationRuntime ExecutionRuntime OptimizationTensor OperationsTesting
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