
Over eight months, Jeong contributed to the Samsung/ONE repository by developing and optimizing quantization workflows, data type support, and model transformation passes. He engineered axis-aware MX quantization across the stack, enhanced resource management, and improved test automation for quantized models. Using C++ and Protocol Buffers, Jeong refactored quantization propagation in the Luci compiler to a phase-based model, enabling more reliable and maintainable quantized inference. His work included extending schema definitions, implementing robust data generation for uint4 paths, and ensuring correctness in shape and quantization transformations. These efforts improved model reliability, diagnostics, and cross-component interoperability within embedded and machine learning systems.

In August 2025, the team delivered quantization enhancements and test framework improvements in Samsung/ONE, elevating data realism, test coverage, and reliability for uint4 paths across SousChef and CircleChef. Key work included Gaussian uint4 data generation, a new FullyConnected U4 test recipe, quantization robustness refactors, and stabilization of the test suite by excluding a problematic U4 test from generation.
In August 2025, the team delivered quantization enhancements and test framework improvements in Samsung/ONE, elevating data realism, test coverage, and reliability for uint4 paths across SousChef and CircleChef. Key work included Gaussian uint4 data generation, a new FullyConnected U4 test recipe, quantization robustness refactors, and stabilization of the test suite by excluding a problematic U4 test from generation.
June 2025 – Samsung/ONE: Delivered key fixes and feature work focusing on resource management, quantization correctness, and CI stability; all changes aimed at improving reliability, model correctness, and faster iteration cycles across the project.
June 2025 – Samsung/ONE: Delivered key fixes and feature work focusing on resource management, quantization correctness, and CI stability; all changes aimed at improving reliability, model correctness, and faster iteration cycles across the project.
May 2025 monthly summary focusing on business value and technical achievements. Highlights include axis-aware MX quantization across multiple components and improved diagnostics, enabling better model performance and faster issue resolution.
May 2025 monthly summary focusing on business value and technical achievements. Highlights include axis-aware MX quantization across multiple components and improved diagnostics, enabling better model performance and faster issue resolution.
April 2025 — Samsung/ONE: MXQuantization support delivered across Circle schema and Luci compiler, enabling export/import of Circle models with MX quantization. The change introduces MXQuantization in the QuantizationDetails union (with axis) and propagates through the Circle schema and luci compiler, setting the stage for broader quantization adoption and interoperability.
April 2025 — Samsung/ONE: MXQuantization support delivered across Circle schema and Luci compiler, enabling export/import of Circle models with MX quantization. The change introduces MXQuantization in the QuantizationDetails union (with axis) and propagates through the Circle schema and luci compiler, setting the stage for broader quantization adoption and interoperability.
March 2025 monthly summary for Samsung/ONE focused on feature delivery and technical excellence. Key work: Luci Interpreter Gather scalar index support implemented, with corresponding configure updates and tests; all changes are traceable to a single commit. No major bugs fixed in this repo for the month; effort centered on delivering robust capability with clear test coverage and maintainable code.
March 2025 monthly summary for Samsung/ONE focused on feature delivery and technical excellence. Key work: Luci Interpreter Gather scalar index support implemented, with corresponding configure updates and tests; all changes are traceable to a single commit. No major bugs fixed in this repo for the month; effort centered on delivering robust capability with clear test coverage and maintainable code.
January 2025 monthly summary for Samsung/ONE. Focused on quantization workflow improvements in the Luci compiler, delivering a robust phase-based execution model that enhances forward propagation of quantization parameters across graphs. This work reduces manual tuning, improves model consistency across deployments, and lays groundwork for scalable quantization across Samsung/ONE workflows.
January 2025 monthly summary for Samsung/ONE. Focused on quantization workflow improvements in the Luci compiler, delivering a robust phase-based execution model that enhances forward propagation of quantization parameters across graphs. This work reduces manual tuning, improves model consistency across deployments, and lays groundwork for scalable quantization across Samsung/ONE workflows.
December 2024 (Samsung/ONE) — Delivered key features in reshape/shaping and Luci quantization paths, enhancing model correctness and quantized inference reliability. Key features: Reshape correctness and shape manipulation improvements (element-count validation in reshape; ExpandDimsToReshapePass scalar axis support; rank update correctness after reshape) and Quantization support and propagation enhancements in Luci (Quantize BroadcastTo activation; propagate qparam backward in onnx-fake quant model; support quantized inputs in ReplaceNonConstFCWithBatchMatMulPass; support Transpose in InsertQuantizeOpOnDTypeMismatch; removal of redundant exception in quantparam propagation). Major bugs fixed: ForwardReshapeToUnaryOpPass bug fix; removal of redundant quantparam propagation exception. Overall impact: stronger model reliability and performance for quantized models; reduced runtime errors and easier maintenance of shape/quantization paths. Technologies/skills demonstrated: shaping passes, quantization propagation, ONNX fake quant modeling, and maintainability improvements across Luci and reshape passes.
December 2024 (Samsung/ONE) — Delivered key features in reshape/shaping and Luci quantization paths, enhancing model correctness and quantized inference reliability. Key features: Reshape correctness and shape manipulation improvements (element-count validation in reshape; ExpandDimsToReshapePass scalar axis support; rank update correctness after reshape) and Quantization support and propagation enhancements in Luci (Quantize BroadcastTo activation; propagate qparam backward in onnx-fake quant model; support quantized inputs in ReplaceNonConstFCWithBatchMatMulPass; support Transpose in InsertQuantizeOpOnDTypeMismatch; removal of redundant exception in quantparam propagation). Major bugs fixed: ForwardReshapeToUnaryOpPass bug fix; removal of redundant quantparam propagation exception. Overall impact: stronger model reliability and performance for quantized models; reduced runtime errors and easier maintenance of shape/quantization paths. Technologies/skills demonstrated: shaping passes, quantization propagation, ONNX fake quant modeling, and maintainability improvements across Luci and reshape passes.
November 2024: Delivered cross-stack MX data type support and performance improvements; improved build hygiene by excluding MX recipes; added quantization utilities and a transpose optimization pass; refined documentation for Circle schema alignment.
November 2024: Delivered cross-stack MX data type support and performance improvements; improved build hygiene by excluding MX recipes; added quantization utilities and a transpose optimization pass; refined documentation for Circle schema alignment.
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