tenstorrent/tt-mlir
Oct 2025 – Apr 2026
5 Months active
Languages Used
C++MLIRPythonCMake
Technical Skills
API DevelopmentBufferizationC++ DevelopmentCode GenerationCompiler DevelopmentDomain Specific Languages (DSL)
Boyana Norris
PROFILE
Boyana Norris
Worked on the tenstorrent/tt-mlir repository, delivering features and fixes across compiler infrastructure, dialect extensions, and build systems. Developed and enhanced MLIR dialects for matrix operations, bufferization, and data movement, including block-sized matmul and TRID-aware NOC operations. Improved reliability and reproducibility by refining test infrastructure and ensuring deterministic address assignment. Contributed to modular build systems using CMake and C++, enabling FlatBuffer-free builds and robust installation workflows. Extended Python integration for API clarity and usability. The work demonstrated depth in compiler design, low-level programming, and testing, resulting in scalable, maintainable code and improved integration for machine learning workloads.
Overall Statistics
Feature vs Bugs
79%Features
Repository Contributions
16Total
Bugs
3
Commits
16
Features
11
Lines of code
4,616
Activity Months5
Your Network
452 peopleWork History
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for tenstorrent/tt-mlir focusing on delivering a new block-sized matrix multiplication capability in TTKernel and API clarity improvements, with associated testing and cross-repo alignment. Key context: The TTKernel dialect now includes a MatmulBlockOp (ttkernel.matmul_block) enabling block-sized matrix multiplies with configurable dimensions and optional transpose. It mirrors the standard tt-metal API and is lowered automatically via TTKernelToEmitCOpaqueRewriter to an emitc.call_opaque("matmul_block", ...). This expands compute engine capabilities while preserving existing lowering paths and integration with the tt-lang ecosystem. Major bug fix: Clarified and corrected the copy_dest_values argument naming and description to align with the tt-metal API, resolving confusion between input/output semantics and improving API consistency. Impact and outcomes: These changes enhance the compute engine’s flexibility for ML workloads, enable more efficient kernel utilization via block-sized matmul, improve API consistency for downstream users, and reduce integration friction. Testing coverage for the new op and API changes has been added to ensure stability across releases. Technologies/skills demonstrated: TTKernel_FPUOp, automatic lowering to opaque calls, alignment with tt-metal API, codebase testing, and API design/clarity work, with cross-repo awareness (tt-lang integration).
1 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for tenstorrent/tt-mlir focusing on delivering a new block-sized matrix multiplication capability in TTKernel and API clarity improvements, with associated testing and cross-repo alignment. Key context: The TTKernel dialect now includes a MatmulBlockOp (ttkernel.matmul_block) enabling block-sized matrix multiplies with configurable dimensions and optional transpose. It mirrors the standard tt-metal API and is lowered automatically via TTKernelToEmitCOpaqueRewriter to an emitc.call_opaque("matmul_block", ...). This expands compute engine capabilities while preserving existing lowering paths and integration with the tt-lang ecosystem. Major bug fix: Clarified and corrected the copy_dest_values argument naming and description to align with the tt-metal API, resolving confusion between input/output semantics and improving API consistency. Impact and outcomes: These changes enhance the compute engine’s flexibility for ML workloads, enable more efficient kernel utilization via block-sized matmul, improve API consistency for downstream users, and reduce integration friction. Testing coverage for the new op and API changes has been added to ensure stability across releases. Technologies/skills demonstrated: TTKernel_FPUOp, automatic lowering to opaque calls, alignment with tt-metal API, codebase testing, and API design/clarity work, with cross-repo awareness (tt-lang integration).
April 2026
March 2026
3 Commits • 2 Features
Mar 1, 2026Concise monthly summary for 2026-03 focusing on business value and technical excellence across TTKernel enhancements and modular TT-MLIR builds. Delivered dialect-level capabilities for block-level tile management and packer data format reconfiguration, improved build modularity by enabling FlatBuffer-free builds, and strengthened test coverage to ensure correctness across conversion paths. These changes reduce downstream integration risk, improve runtime correctness of packing/formatting, and enable leaner builds for TTKernel/TTCore subsets.
March 2026
3 Commits • 2 Features
Mar 1, 2026Concise monthly summary for 2026-03 focusing on business value and technical excellence across TTKernel enhancements and modular TT-MLIR builds. Delivered dialect-level capabilities for block-level tile management and packer data format reconfiguration, improved build modularity by enabling FlatBuffer-free builds, and strengthened test coverage to ensure correctness across conversion paths. These changes reduce downstream integration risk, improve runtime correctness of packing/formatting, and enable leaner builds for TTKernel/TTCore subsets.
December 2025
4 Commits • 2 Features
Dec 1, 2025Monthly summary for 2025-12 focusing on business value and technical achievements for tenstorrent/tt-mlir. Highlights cover four core areas: (1) Feature delivery and architecture extensions enabling higher performance and better tooling; (2) Bug fixes and reliability improvements; (3) Overall impact on product velocity, reproducibility, and maintainability; (4) Technologies and skills demonstrated across MLIR EmitC, tt-metal, and kernel dialects. Key outcomes: - Stability and test reliability improvements by skipping StableHLO-dependent tests when StableHLO support is disabled, reducing false failures and shortening CI feedback loops. - New TRID-aware NOC operations in the ttkernel dialect, with verifiers for TRID and NOC values, EmitC lowering to tt-metal TRID APIs, and comprehensive tests, enabling fine-grained DMA synchronization and overlapped data-transfer/compute without global barriers. - Deterministic address assignment in D2MAllocate by replacing DenseMap with MapVector, improving reproducibility and debugging across runs. - EmitC TensorAccessorArgs chaining support, including prev_args chaining, optional override expressions, and new verification rules; introduces a breaking but compatible path to emitC.verbatim for improved code generation and maintainability. Business value: - Increased reliability of the test suite, reducing churn and speeding up validation. - Performance and scalability gains from TRID-aware NOC operations and better DMA/compute overlap. - Predictable builds and test results due to deterministic address assignment. - Expanded EmitC capabilities enabling more expressive and maintainable code-gen for tt-metal kernels. Technologies/skills demonstrated: - MLIR/EmitC, tt-metal API integration, dialect extensions (ttkernel), verification mechanics, and test strategy. - C++ patterns for offset management, stable test configurations, and deterministic data structures. - Commit-driven traceability with references to key changes for traceability and review.
4 Commits • 2 Features
Dec 1, 2025Monthly summary for 2025-12 focusing on business value and technical achievements for tenstorrent/tt-mlir. Highlights cover four core areas: (1) Feature delivery and architecture extensions enabling higher performance and better tooling; (2) Bug fixes and reliability improvements; (3) Overall impact on product velocity, reproducibility, and maintainability; (4) Technologies and skills demonstrated across MLIR EmitC, tt-metal, and kernel dialects. Key outcomes: - Stability and test reliability improvements by skipping StableHLO-dependent tests when StableHLO support is disabled, reducing false failures and shortening CI feedback loops. - New TRID-aware NOC operations in the ttkernel dialect, with verifiers for TRID and NOC values, EmitC lowering to tt-metal TRID APIs, and comprehensive tests, enabling fine-grained DMA synchronization and overlapped data-transfer/compute without global barriers. - Deterministic address assignment in D2MAllocate by replacing DenseMap with MapVector, improving reproducibility and debugging across runs. - EmitC TensorAccessorArgs chaining support, including prev_args chaining, optional override expressions, and new verification rules; introduces a breaking but compatible path to emitC.verbatim for improved code generation and maintainability. Business value: - Increased reliability of the test suite, reducing churn and speeding up validation. - Performance and scalability gains from TRID-aware NOC operations and better DMA/compute overlap. - Predictable builds and test results due to deterministic address assignment. - Expanded EmitC capabilities enabling more expressive and maintainable code-gen for tt-metal kernels. Technologies/skills demonstrated: - MLIR/EmitC, tt-metal API integration, dialect extensions (ttkernel), verification mechanics, and test strategy. - C++ patterns for offset management, stable test configurations, and deterministic data structures. - Commit-driven traceability with references to key changes for traceability and review.
December 2025
November 2025
3 Commits • 2 Features
Nov 1, 2025Month: 2025-11 — This period focused on delivering key features for D2M DST workflows, strengthening build/install reliability, and establishing a foundation for future performance improvements. Business value includes more robust data movement, safer bufferization, easier deployment, and scalable DST analysis.
November 2025
3 Commits • 2 Features
Nov 1, 2025Month: 2025-11 — This period focused on delivering key features for D2M DST workflows, strengthening build/install reliability, and establishing a foundation for future performance improvements. Business value includes more robust data movement, safer bufferization, easier deployment, and scalable DST analysis.
October 2025
5 Commits • 4 Features
Oct 1, 2025October 2025 monthly summary focusing on key accomplishments, features delivered, and impact for tenstorrent/tt-mlir. The month prioritized expanding feature support, optimizing access patterns, and extending Python integration to strengthen the end-to-end MLIR pipeline and user adoption.
5 Commits • 4 Features
Oct 1, 2025October 2025 monthly summary focusing on key accomplishments, features delivered, and impact for tenstorrent/tt-mlir. The month prioritized expanding feature support, optimizing access patterns, and extending Python integration to strengthen the end-to-end MLIR pipeline and user adoption.
October 2025
Activity
Loading activity data...
Quality Metrics
Correctness96.2%
Maintainability86.2%
Architecture95.0%
Performance85.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
C++CMakeMLIRPython
Technical Skills
API DevelopmentBufferizationBuild SystemsC++C++ DevelopmentC++ developmentCMakeCode GenerationCompiler DesignCompiler DevelopmentDomain Specific Languages (DSL)Intermediate RepresentationIntermediate Representation (IR) ManipulationIntermediate Representation ConversionMLIR
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline