tenstorrent/tt-mlir
May 2025 – Sep 2025
3 Months active
Languages Used
C++MLIRPython
Technical Skills
Device ManagementEmbedded SystemsCode GenerationCode RefactoringCompiler DevelopmentDialect Conversion
Andjela Milovanovic
PROFILE
Andjela Milovanovic
Aleksandar Milovanovic contributed to the tenstorrent/tt-mlir repository by developing and refining Python code generation pipelines for machine learning models. He implemented the EmitPy dialect to encode Python syntax within MLIR, enabling direct emission of Python code for models such as MNIST and ResNet. His work included refactoring the TTNN-to-EmitPy conversion using the EmitPyTTNNEmitter, which improved maintainability and expanded operation coverage for complex models. Additionally, Aleksandar addressed device initialization reliability by correcting L1 memory configuration, resulting in more stable embedded system startups. His efforts demonstrated depth in compiler development, code generation, and embedded systems using C++, MLIR, and Python.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
4Total
Bugs
1
Commits
4
Features
2
Lines of code
5,197
Activity Months3
Your Network
390 people
Work History
September 2025
1 Commits • 1 Features
Sep 1, 2025Month: 2025-09 — Focused on enabling Python code generation for ResNet models via EmitPy integration in the tt-mlir repository. Key work includes integrating a TTNN to EmitPy emitter and expanding operation coverage to translate more complex models, laying groundwork for future optimizer support. This work strengthens the bridge between model definitions and executable Python pipelines, accelerating deployment of ResNet-style architectures. No major bugs reported or fixed in this period.
1 Commits • 1 Features
Sep 1, 2025Month: 2025-09 — Focused on enabling Python code generation for ResNet models via EmitPy integration in the tt-mlir repository. Key work includes integrating a TTNN to EmitPy emitter and expanding operation coverage to translate more complex models, laying groundwork for future optimizer support. This work strengthens the bridge between model definitions and executable Python pipelines, accelerating deployment of ResNet-style architectures. No major bugs reported or fixed in this period.
September 2025
July 2025
2 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for tenstorrent/tt-mlir. Key features delivered include the EmitPy Python code generation (MLIR dialect) for the MNIST TTIR pipeline, introducing the EmitPy dialect to encode Python syntax within MLIR and enabling direct Python code emission. This work includes a complete TTIR -> EmitPy conversion pipeline and a subsequent Python code emission step for the MNIST model. Additionally, TTNN-to-EmitPy conversion was refactored to use the EmitPyTTNNEmitter, consolidating the conversion pathway and improving maintainability.
July 2025
2 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for tenstorrent/tt-mlir. Key features delivered include the EmitPy Python code generation (MLIR dialect) for the MNIST TTIR pipeline, introducing the EmitPy dialect to encode Python syntax within MLIR and enabling direct Python code emission. This work includes a complete TTIR -> EmitPy conversion pipeline and a subsequent Python code emission step for the MNIST model. Additionally, TTNN-to-EmitPy conversion was refactored to use the EmitPyTTNNEmitter, consolidating the conversion pathway and improving maintainability.
May 2025
1 Commits
May 1, 2025Month 2025-05: Improved device initialization reliability in tenstorrent/tt-mlir by fixing the default L1 small size to 32 KB (1 << 15). This correction ensures alignment with the tt-mlir runtime configuration and prevents 0-sized defaults during device opening in the ttnn-standalone workflow. Result: more stable startup, fewer initialization failures, and smoother end-to-end workloads. Commit reference: 8b9e965d48b528808203515ed398ef777902676b (Fix set default l1 small size on opening the device, ##3214).
1 Commits
May 1, 2025Month 2025-05: Improved device initialization reliability in tenstorrent/tt-mlir by fixing the default L1 small size to 32 KB (1 << 15). This correction ensures alignment with the tt-mlir runtime configuration and prevents 0-sized defaults during device opening in the ttnn-standalone workflow. Result: more stable startup, fewer initialization failures, and smoother end-to-end workloads. Commit reference: 8b9e965d48b528808203515ed398ef777902676b (Fix set default l1 small size on opening the device, ##3214).
May 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++MLIRPython
Technical Skills
Code GenerationCode RefactoringCompiler DevelopmentDevice ManagementDialect ConversionDialect DesignEmbedded SystemsEmitPyMLIRPythonPython EmissionTTNN
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