tenstorrent/tt-mlir
Jul 2025 – Mar 2026
7 Months active
Languages Used
C++LLVM IRMLIRPython
Technical Skills
Code AnalysisMLIRUnit TestingC++ developmentCompiler DevelopmentCompiler Optimization
Bojana Malesevic
PROFILE
Bojana Malesevic
Bojan Malesevic contributed to the tenstorrent/tt-mlir, tt-xla, and tt-forge repositories by developing and optimizing backend compiler features for machine learning workloads. He engineered memory-aware optimizations, deterministic fallback mechanisms, and distributed sharding for operations like Conv2D, RoPE, and Gelu, improving reliability and scalability under memory constraints. Using C++, MLIR, and Python, Bojan enhanced performance metrics automation, integrated system descriptors, and strengthened layout and data type consistency across the compilation pipeline. His work addressed complex memory management and validation challenges, resulting in robust, testable solutions that improved benchmarking accuracy, error recovery, and system observability for production inference deployments.
Overall Statistics
Feature vs Bugs
60%Features
Repository Contributions
28Total
Bugs
8
Commits
28
Features
12
Lines of code
4,872
Activity Months8
Your Network
461 peopleWork History
March 2026
1 Commits
Mar 1, 2026March 2026 monthly summary for tenstorrent/tt-mlir focused on improving robustness and consistency of the TTNN-IR to FlatBuffer conversion path. Delivered a memory configuration fix for the sort operation and strengthened alignment with existing op patterns to enhance memory management reliability across the pipeline.
1 Commits
Mar 1, 2026March 2026 monthly summary for tenstorrent/tt-mlir focused on improving robustness and consistency of the TTNN-IR to FlatBuffer conversion path. Delivered a memory configuration fix for the sort operation and strengthened alignment with existing op patterns to enhance memory management reliability across the pipeline.
March 2026
February 2026
6 Commits • 1 Features
Feb 1, 2026February 2026 (tenstorrent/tt-mlir): Delivered deterministic fallbacks and robustness improvements across the TTNN/Optimizer stack, plus memory-aware fallbacks for ConvTranspose2d. These changes improved determinism, error recovery, and multi-output layout handling while aligning validation with Conv2d behavior. The work directly enhances reliability in production inference, reduces nondeterministic behavior, and strengthens memory-pressure resilience.
February 2026
6 Commits • 1 Features
Feb 1, 2026February 2026 (tenstorrent/tt-mlir): Delivered deterministic fallbacks and robustness improvements across the TTNN/Optimizer stack, plus memory-aware fallbacks for ConvTranspose2d. These changes improved determinism, error recovery, and multi-output layout handling while aligning validation with Conv2d behavior. The work directly enhances reliability in production inference, reduces nondeterministic behavior, and strengthens memory-pressure resilience.
January 2026
4 Commits • 1 Features
Jan 1, 2026Month: 2026-01. This month focused on enhancing memory-aware optimization and strengthening layout/dtype correctness in the TT-MLIR optimizer to improve reliability under constrained memory and backend transitions.
4 Commits • 1 Features
Jan 1, 2026Month: 2026-01. This month focused on enhancing memory-aware optimization and strengthening layout/dtype correctness in the TT-MLIR optimizer to improve reliability under constrained memory and backend transitions.
January 2026
December 2025
7 Commits • 4 Features
Dec 1, 2025December 2025 performance highlights across tenstorrent repositories TT-FORGE, TT-XLA, and TT-MLIR. Delivered end-to-end performance measurement and reporting enhancements, with automated collection and aggregation of TTNN performance metrics, robust per-graph metric handling, and improved observability across benchmarks. Implemented distributed sharding for RoPE and Gelu to enable scalable execution for large language models, accompanied by validation tests (e.g., Llama 3.2). Fixed a critical embedding output shape validation regression in OpModel to restore correctness after tt-metal uplifts. Introduced optimizer fallback improvements that reduce build times and improve error visibility. These changes collectively improve benchmarking accuracy, build reliability, and system observability, delivering clear business value in performance-sensitive ML deployment pipelines.
December 2025
7 Commits • 4 Features
Dec 1, 2025December 2025 performance highlights across tenstorrent repositories TT-FORGE, TT-XLA, and TT-MLIR. Delivered end-to-end performance measurement and reporting enhancements, with automated collection and aggregation of TTNN performance metrics, robust per-graph metric handling, and improved observability across benchmarks. Implemented distributed sharding for RoPE and Gelu to enable scalable execution for large language models, accompanied by validation tests (e.g., Llama 3.2). Fixed a critical embedding output shape validation regression in OpModel to restore correctness after tt-metal uplifts. Introduced optimizer fallback improvements that reduce build times and improve error visibility. These changes collectively improve benchmarking accuracy, build reliability, and system observability, delivering clear business value in performance-sensitive ML deployment pipelines.
November 2025
4 Commits • 3 Features
Nov 1, 2025Monthly summary for 2025-11 focused on TT-MLIR and TT-XLA performance enhancements, sharding, and metrics instrumentation. Highlights cover delivered features, major fixes, and cross-repo impact with clear business value and technical outcomes.
4 Commits • 3 Features
Nov 1, 2025Monthly summary for 2025-11 focused on TT-MLIR and TT-XLA performance enhancements, sharding, and metrics instrumentation. Highlights cover delivered features, major fixes, and cross-repo impact with clear business value and technical outcomes.
November 2025
September 2025
1 Commits
Sep 1, 2025Monthly work summary for 2025-09 highlighting system descriptor improvements in tt-forge-fe (tenstorrent/tt-forge-fe).
September 2025
1 Commits
Sep 1, 2025Monthly work summary for 2025-09 highlighting system descriptor improvements in tt-forge-fe (tenstorrent/tt-forge-fe).
August 2025
4 Commits • 2 Features
Aug 1, 2025Concise monthly summary for performance review focusing on business value and technical achievements for August 2025 (tt-mlir):
4 Commits • 2 Features
Aug 1, 2025Concise monthly summary for performance review focusing on business value and technical achievements for August 2025 (tt-mlir):
August 2025
July 2025
1 Commits • 1 Features
Jul 1, 20252025-07 Monthly summary for tenstorrent/tt-mlir focusing on feature delivery and testing improvements with traceability to commits.
July 2025
1 Commits • 1 Features
Jul 1, 20252025-07 Monthly summary for tenstorrent/tt-mlir focusing on feature delivery and testing improvements with traceability to commits.
Activity
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Quality Metrics
Correctness95.4%
Maintainability82.6%
Architecture86.8%
Performance83.2%
AI Usage26.4%
Skills & Technologies
Programming Languages
BashC++LLVM IRMLIRPython
Technical Skills
Backend DevelopmentC++C++ developmentC++ programmingCode AnalysisCompiler DevelopmentCompiler OptimizationCompiler optimizationDebuggingEmbedded SystemsJSON SerializationLow-Level ProgrammingMLIRMLIR optimizationMachine Learning
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline
tenstorrent/tt-xla
Nov 2025 – Dec 2025
2 Months active
Languages Used
C++Bash
Technical Skills
C++ developmentcompiler designperformance optimizationbash scriptingscriptingsoftware engineering
tenstorrent/tt-forge
Dec 2025 – Dec 2025
1 Month active
Languages Used
Python
Technical Skills
Pythonbenchmarkingdata processingdata visualizationfile handlingperformance analysis
tenstorrent/tt-forge-fe
Sep 2025 – Sep 2025
1 Month active
Languages Used
C++
Technical Skills
Compiler DevelopmentMLIRSystem Integration