EPFL-LAP/dynamatic
Apr 2025 – Jun 2025
2 Months active
Languages Used
C++MLIRMarkdown
Technical Skills
Compiler DevelopmentLow-Level OptimizationMLIRCompiler DesignDocumentationHardware Description Languages
nikolatmic
PROFILE
Nikolatmic
Nikolatomic worked on the EPFL-LAP/dynamatic repository, delivering foundational support for multi-output MLIR operations in the cf-to-handshake lowering pass. Over two months, he implemented argument classification logic in C++ and MLIR, enabling robust categorization of function arguments and correct propagation through the handshake pipeline. He extended the lowering to support multi-result handshake.instance operations, allowing richer IR transformations at the C-level. His work included comprehensive documentation in Markdown and automated tests to verify instantiation correctness and cleanup. The depth of his contributions improved the reliability and expressiveness of the compiler infrastructure, laying groundwork for future multi-output enhancements.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
2
Lines of code
597
Activity Months2
Your Network
62 peopleSame Organization
@gmx.ch48
Work History
June 2025
3 Commits • 1 Features
Jun 1, 2025June 2025 performance-summary for EPFL-LAP/dynamatic: Delivered the core enhancement for MLIR Op Instantiation at the C-Level with multi-output support (CfToHandshake lowering). This includes lowering placeholder functions into multi-result handshake.instance ops, enabling richer MLIR workflows and more expressive IR transformations. A comprehensive documentation package was published detailing usage, conventions, pass logic, and a full end-to-end example. Automated tests were added to verify correct instantiation generation (inputs/outputs/rewiring) and ensure removal of unused function calls.
3 Commits • 1 Features
Jun 1, 2025June 2025 performance-summary for EPFL-LAP/dynamatic: Delivered the core enhancement for MLIR Op Instantiation at the C-Level with multi-output support (CfToHandshake lowering). This includes lowering placeholder functions into multi-result handshake.instance ops, enabling richer MLIR workflows and more expressive IR transformations. A comprehensive documentation package was published detailing usage, conventions, pass logic, and a full end-to-end example. Automated tests were added to verify correct instantiation generation (inputs/outputs/rewiring) and ensure removal of unused function calls.
June 2025
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for EPFL-LAP/dynamatic: Delivered argument classification for the cf-to-handshake pass to support multi-output MLIR operations. Implemented classification logic that categorizes function arguments into inputs, outputs, and parameters, and mapped output arguments to their consuming operations, enabling correct propagation through the pipeline and paving the way for future multi-output support. This work enhances correctness, reduces downstream integration effort, and improves overall handshaking reliability.
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for EPFL-LAP/dynamatic: Delivered argument classification for the cf-to-handshake pass to support multi-output MLIR operations. Implemented classification logic that categorizes function arguments into inputs, outputs, and parameters, and mapped output arguments to their consuming operations, enabling correct propagation through the pipeline and paving the way for future multi-output support. This work enhances correctness, reduces downstream integration effort, and improves overall handshaking reliability.
Activity
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Quality Metrics
Correctness95.0%
Maintainability85.0%
Architecture90.0%
Performance75.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++MLIRMarkdown
Technical Skills
Compiler DesignCompiler DevelopmentDocumentationHardware Description LanguagesIR TransformationLow-Level OptimizationMLIRTesting
Repositories Contributed To
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