tenstorrent/tt-mlir
Nov 2024 – Dec 2024
2 Months active
Languages Used
C++MLIRCMakeScript
Technical Skills
Compiler DevelopmentEmbedded SystemsLow-Level OptimizationMLIR DialectsMemory ManagementAlgorithm Design
Filip Bajraktari
PROFILE
Filip Bajraktari
Over a two-month period, contributed to the tenstorrent/tt-mlir repository by developing and refining the L1 Interleaved Memory Layout Policy for the TTNN backend. This work involved refactoring the backend to support multiple memory policies, enabling more flexible and scalable memory management for embedded systems. Leveraging C++, MLIR, and advanced compiler optimization techniques, implemented a greedy join-node optimization and introduced the BFInterleavedPolicy to handle complex fork-join scenarios. Enhanced support for memRef of L1 interleaved tensors and improved memory layout calculations, directly addressing locality and tiling efficiency challenges in tensor operations while laying groundwork for future backend optimization.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
2
Lines of code
2,464
Activity Months2
Your Network
452 peopleWork History
December 2024
3 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for tenstorrent/tt-mlir focusing on L1 Interleaved Memory Layout Policy Improvements and their business value.
3 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for tenstorrent/tt-mlir focusing on L1 Interleaved Memory Layout Policy Improvements and their business value.
December 2024
November 2024
1 Commits • 1 Features
Nov 1, 2024Month: 2024-11 — Key feature delivered: TTNN Memory Layout Policy: L1 Interleaved in the TT-MLIR backend, with a refactor to support multiple memory policies. This enables flexible memory management and optimization for TTNN workloads, improving scalability and resource efficiency. Delivered via commit c038025ed0bf7d71f0c09fb3e47ce2c936ba76e2 ("L1 interleaved policy (#1117)"), enabling future policy experiments and broader TTNN backend optimization.
November 2024
1 Commits • 1 Features
Nov 1, 2024Month: 2024-11 — Key feature delivered: TTNN Memory Layout Policy: L1 Interleaved in the TT-MLIR backend, with a refactor to support multiple memory policies. This enables flexible memory management and optimization for TTNN workloads, improving scalability and resource efficiency. Delivered via commit c038025ed0bf7d71f0c09fb3e47ce2c936ba76e2 ("L1 interleaved policy (#1117)"), enabling future policy experiments and broader TTNN backend optimization.
Activity
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Quality Metrics
Correctness85.0%
Maintainability82.6%
Architecture87.6%
Performance82.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CMakeScriptMLIR
Technical Skills
Algorithm DesignC++Compiler DevelopmentCompiler OptimizationEmbedded SystemsLow-Level OptimizationMLIRMLIR DialectsMemory ManagementOptimizer DevelopmentTensor Operations
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