KULeuven-MICAS/snax-mlir
May 2025 – May 2025
1 Month active
Languages Used
CPythonYAML
Technical Skills
Build AutomationC ProgrammingCI/CDKernel DevelopmentMLIRPython Scripting
Ryan Antonio
PROFILE
Ryan Antonio
Ryan Antonio developed an end-to-end Multilayer Perceptron (MLP) integration for the KULeuven-MICAS/snax-mlir repository, focusing on reproducible ML kernel development. He defined the MLP kernel in C, automated MLIR code generation with Python scripting, and established a Snakemake-based build workflow. By integrating the MLP path into the CI/CD pipeline, Ryan enabled automated builds and end-to-end validation on each commit, improving reliability and regression coverage. His work reduced manual modeling effort and accelerated experimentation by supporting rapid iteration over kernel variants. The project demonstrated depth in build automation, kernel development, and continuous integration using C, Python, and MLIR.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
381
Activity Months1
Your Network
4 peopleSame Organization
@eee.upd.edu.ph1
Shared Repositories
3
Work History
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary focused on delivering an end-to-end Multilayer Perceptron (MLP) integration into the KULeuven-MICAS/snax-mlir repository, with CI build automation and end-to-end validation. Business value centers on reproducible MLIR generation, automated builds, and validated execution paths to accelerate experimentation and reduce time-to-production for ML kernels.
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary focused on delivering an end-to-end Multilayer Perceptron (MLP) integration into the KULeuven-MICAS/snax-mlir repository, with CI build automation and end-to-end validation. Business value centers on reproducible MLIR generation, automated builds, and validated execution paths to accelerate experimentation and reduce time-to-production for ML kernels.
May 2025
Activity
Loading activity data...
Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CPythonYAML
Technical Skills
Build AutomationC ProgrammingCI/CDKernel DevelopmentMLIRPython Scripting
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline