tenstorrent/tt-mlir
Oct 2025 – Jun 2026
4 Months active
Languages Used
C++MLIRPython
Technical Skills
Compiler DevelopmentDialect ExtensionLow-Level ProgrammingTensor Operationscompiler designhardware acceleration
Alex Richins
PROFILE
Alex Richins
Worked on the tenstorrent/tt-mlir repository, delivering four compiler features over four months focused on hardware-accelerated numeric computing and flexible memory operations. Developed cross-dialect bitwise NOT support for i32, expanded TTKernel with 16 unary math operations, and introduced device-side comparison ops for bfloat16 and float32. Enhanced memory management by parameterizing pointer widths and enabling sub-word load/store, improving bandwidth efficiency and code generation. The work involved C++, MLIR, and Python, emphasizing low-level programming, dialect extension, and rigorous testing. Each feature included comprehensive test coverage and careful integration into lowering paths, supporting maintainability and robust end-to-end translation across backends.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
4
Lines of code
1,063
Activity Months4
Your Network
479 peopleWork History
June 2026
2 Commits • 1 Features
Jun 1, 2026June 2026 monthly summary for tenstorrent/tt-mlir focused on TTKernel numeric comparison ops and test coverage. Implemented device-side comparisons for bfloat16 and float32 values to enable conditional logic based on raw bit patterns, aligned argument typing, and executed comprehensive test suites to ensure correctness across dialect, conversion, and end-to-end translation. This work improves device-side programmability, consistency with TTKernel conventions, and overall robustness of numeric operations.
2 Commits • 1 Features
Jun 1, 2026June 2026 monthly summary for tenstorrent/tt-mlir focused on TTKernel numeric comparison ops and test coverage. Implemented device-side comparisons for bfloat16 and float32 values to enable conditional logic based on raw bit patterns, aligned argument typing, and executed comprehensive test suites to ensure correctness across dialect, conversion, and end-to-end translation. This work improves device-side programmability, consistency with TTKernel conventions, and overall robustness of numeric operations.
June 2026
May 2026
1 Commits • 1 Features
May 1, 2026May 2026 TT MLIR monthly summary focused on enhancing memory access flexibility and cross-language code generation. Implemented parameterized L1AddrPtrType with 8/16/32-bit widths and removed the volatile qualifier from L1 pointer types, enabling safe sub-word memory operations. Extended L1 load/store infrastructure to support i8 and i16, widened value types, and aligned dialects, EmitC conversion, and D2M lowering to width-aware paths. Expanded test coverage with i8/i16 scenarios and updated checks to reflect the new type system. These changes improve memory bandwidth efficiency, support bf16 workflows, and strengthen end-to-end code generation.
May 2026
1 Commits • 1 Features
May 1, 2026May 2026 TT MLIR monthly summary focused on enhancing memory access flexibility and cross-language code generation. Implemented parameterized L1AddrPtrType with 8/16/32-bit widths and removed the volatile qualifier from L1 pointer types, enabling safe sub-word memory operations. Extended L1 load/store infrastructure to support i8 and i16, widened value types, and aligned dialects, EmitC conversion, and D2M lowering to width-aware paths. Expanded test coverage with i8/i16 scenarios and updated checks to reflect the new type system. These changes improve memory bandwidth efficiency, support bf16 workflows, and strengthen end-to-end code generation.
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for tenstorrent/tt-mlir focused on hardware-backed math operations via TTKernel dialect enhancements. Delivered 16 unary tile operations (exp2, expm1, log1p, square, softsign, signbit, selu, frac, trunc and respective init+tile pairs) to support lowering unary math ops to the LLK compute API in tt-metal. Implemented pattern where each op has an init+tile pair, with specific handling for ops requiring extra parameters (e.g., Selu with scale/alpha). Updated TTKernelToEmitC.cpp to register all new ops and added lit test cases in ttkernel.mlir to verify EmitC conversion. All changes are covered by a dedicated tests suite and aligned with a PR referencing #7914. This work enables hardware-accelerated unary math operations and paves the path for improved performance and portability across backends.
1 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for tenstorrent/tt-mlir focused on hardware-backed math operations via TTKernel dialect enhancements. Delivered 16 unary tile operations (exp2, expm1, log1p, square, softsign, signbit, selu, frac, trunc and respective init+tile pairs) to support lowering unary math ops to the LLK compute API in tt-metal. Implemented pattern where each op has an init+tile pair, with specific handling for ops requiring extra parameters (e.g., Selu with scale/alpha). Updated TTKernelToEmitC.cpp to register all new ops and added lit test cases in ttkernel.mlir to verify EmitC conversion. All changes are covered by a dedicated tests suite and aligned with a PR referencing #7914. This work enables hardware-accelerated unary math operations and paves the path for improved performance and portability across backends.
April 2026
October 2025
1 Commits • 1 Features
Oct 1, 2025Monthly summary for 2025-10 focusing on tt-mlir feature work: delivered cross-dialect Bitwise NOT support for i32, including new ops, type handling, lowering, and tests. This work enhances correctness and consistency across dialects, enabling broader optimization and codegen paths. The effort also improves test coverage and maintainability in the tt-mlir repo.
October 2025
1 Commits • 1 Features
Oct 1, 2025Monthly summary for 2025-10 focusing on tt-mlir feature work: delivered cross-dialect Bitwise NOT support for i32, including new ops, type handling, lowering, and tests. This work enhances correctness and consistency across dialects, enabling broader optimization and codegen paths. The effort also improves test coverage and maintainability in the tt-mlir repo.
Activity
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Quality Metrics
Correctness98.0%
Maintainability84.0%
Architecture92.0%
Performance84.0%
AI Usage44.0%
Skills & Technologies
Programming Languages
C++MLIRPython
Technical Skills
Compiler DevelopmentDialect ExtensionLow-Level ProgrammingTensor Operationscompiler designhardware accelerationlow-level programmingmemory managementnumeric computingnumerical methodstesting
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