May 2026 monthly summary for tenstorrent/tt-mlir focused on stabilizing the D2M path, expanding performance-oriented features, and strengthening testing and maintainability to accelerate GPT-OSS bring-up and long-term reliability. 1) Key features delivered - Elementwise Fusion with Implicit Broadcasts: enabled fusion across tile_bcast operands by removing the D2M_SkipOpEltwiseFusionTrait, allowing multiple broadcasted operations to fuse into a single d2m.generic kernel, reducing kernel launch overhead and improving throughput. - Default L1 Accumulation in D2M: enabled L1 accumulation by default with a safe gating model and updated tests; improves numerical correctness and performance across large matmuls with minimal user impact. - DST access and memory-management improvements: substantial overhauls to InsertDstRegisterAccess, including unification and refactoring (split into Shared/Scheduled/Unscheduled in dedicated subdirs), reworked DstStackAllocator with explicit slot management, and centralized DST access emission to improve correctness and throughput. 2) Major bugs fixed - D2M Integration Stability and Correctness: addressed bring-up blockers (e.g., excluding ttnn.gt from d2m_subgraph), corrected read-after-write hazards in fused elementwise regions, and completed DST passes refactors to ensure correctness and maintainability. - Cross-type elementwise alignment: explicit handling for mismatched operand/result types in cross-type ops, preventing out-of-type writes and ensuring consistent layout element types across D2M stages. - JIT performance metrics reporting: fixed 0 ns / 0 programs reporting by aggregating per-device perf data to produce accurate jit_kernel_duration_ns and perf_ratio values. 3) Overall impact and accomplishments - End-to-end reliability: stabilizing the D2M path reduces bring-up time for GPT-OSS and future models, enabling faster iteration and safer deployment. - Performance uplift: implicit-broadcast fusion and optimized DST memory management reduce kernel count and improve throughput for large, broadcast-heavy workloads. - Maintainability and scalability: reorganized D2M transforms into dedicated subdirs, unified DST access model, and added regression tests, paving the way for easier maintenance and future enhancements. - Test and validation rigor: expanded lit tests and end-to-end snippets, ensuring coverage of cross-type and broadcast scenarios; improved JIT/perf reporting for more trustworthy telemetry. 4) Technologies/skills demonstrated - MLIR and D2M transform engineering: elementwise fusion, cross-type type alignment, scratch-slot memory management, and DST register access pipelines. - Performance engineering: JIT/perf telemetry, implicit-broadcast fusion, and L1 accumulation gating treated as correctness-critical and performance-positive changes. - Testing and reliability: extensive lit-based testing, end-to-end snippets for GPT OSS bring-up, and regression tests around scratch-slot behavior and DST pool management.

April 2026 (tenstorrent/tt-mlir): Delivered a major MatMul transformation refactor that decouples DST handling from tile_matmul_block insertion, enabling clearer separation of concerns and more reliable transformations. Implemented a modular pass chain (d2m-insert-dst-register-access, d2m-insert-tile-matmul-block, d2m-linalg-to-affine) and integrated with the TTMetal pipeline to govern tile-based matmul lowering. Ensured matmul operations are consistently lowered via linalg_to_affine, with stricter eligibility checks and improved error handling for unresolved linalg.generic ops. Updated and expanded tests to cover both block-based and non-block-based paths. Result: a more maintainable, scalable, and debuggable transformation pipeline with earlier detection of misconfigurations and clearer responsibilities between passes.

March 2026 Monthly Summary for tenstorrent/tt-mlir This month focused on advancing the D2M path with richer optimization and reduction support, strengthening JIT robustness, expanding performance visibility, and addressing CI packaging and maintenance gaps. Key work spanned D2M optimization, native ttir reductions, JIT improvements, performance benchmarking, and packaging/documentation hygiene, all aimed at delivering higher throughput, lower latency, and more reliable builds for TTNN/D2M workflows. Key outcomes include improved deployment of D2M in L1 optimization chains, end-to-end support for ttir.mean and ttir.min reductions, robust JIT tracing with enhanced support for CCLs and fallback execution, and an automated nightly performance collection pipeline feeding Superset dashboards for observability. In addition, packaging fixes and codebase cleanup reduce CI friction and improve long-term maintainability. Top achievements for the month: - Enabled D2M Subgraph Op participation in L1 optimization chains with a cost model, native mean support, and a min-decomposition pass, reducing unnecessary layout changes and improving fusion opportunities. - Brought native D2M support for ttir.mean and introduced end-to-end TTIR→D2M→TTKernel→EmitC pathways, including lit tests and expanded test coverage for mean reductions. - Strengthened JIT robustness and coverage: fixed type hint resolution in tracing, added support for collective ops (CCL) in ttnn-jit tracing, and introduced a fallback mode to maintain execution when JIT paths fail. - Implemented nightly performance measurement and reporting: automated perf collection for JIT vs TTNN, matmul and subgraph benchmarks, and Superset dashboard integration for performance visibility. - Resolved packaging issues and cleaned up legacy code: fixed pykernel wheel packaging, added missing _src package, and removed unused code paths to stabilize nightly and CI jobs. Technologies/skills demonstrated: - D2M/TTNN integration (D2MOpCostModel, TileReduce ops, mean/min reductions, L1 optimization), MLIR dialects, and TTKernel mappings. - JIT tooling and tracing enhancements (type hints, mesh shape propagation for CCLs, fallback mechanics). - Performance engineering and telemetry (nightly perf suite, Superset dashboards, per-case benchmarking). - CI hygiene and packaging (pykernel wheel, packaging scripts, tests maintenance). Business value realized: improved performance potential through richer fusion and reduction pathways, increased reliability via fallback execution and CI fixes, and better observability and decision-making through automated performance dashboards and tests.

February 2026 monthly summary highlighting key features delivered, major bugs fixed, impact, and technologies demonstrated. This period focused on expanding JIT tracing, memory management, D2M-optimizer integration, and CI stability to deliver measurable business value: improved observability, safer memory planning, and more efficient execution in TTNN-JIT.

January 2026 monthly summary focused on delivering a single, robust IR-generation path, improving reliability of uplift workstreams, and expanding test coverage. The month delivered a tracing-based approach for TTNN-JIT IR generation, consolidated uplift workflow for XLA, and targeted stability improvements, with strong emphasis on business value and maintainability.

December 2025 (2025-12): Delivered core TTNN-JIT enhancements in tenstorrent/tt-mlir, significantly strengthening graph compilation, modularity, and reliability. Introduced levelized graph traversal, reduction and composite ops, and a flexible operation registry to improve maintainability and future extensibility. Implemented a graph-capture return modifier to ensure accurate output metadata. Addressed critical graph-capture bugs and tensor-layout issues across 3D+ ranks, and stabilized builds/docs for TTMLIR.

Month: 2025-11. Focus: TTNN-JIT IR Graph Capture with Control-Flow Support in tenstorrent/tt-mlir. This work delivers a scalable IR generation path for JIT-compiled graphs, enabling control-flow constructs and improving performance/maintainability of the JIT stack. The effort also strengthens test coverage and compatibility with the existing AST-based IR pipeline to minimize risk when migrating models to the new path.

Month: 2025-10 — Delivered Python wheel packaging and distribution support for ttnn-jit in the tenstorrent/tt-mlir repo, establishing a repeatable build and test flow for wheel-based installs. The work enables easy distribution of the ttnn-jit Python module, integrates wheel build into CI/CD, and adds testing steps that install and exercise the wheel during validation. Also added packaging/setup files to formalize the Python module and streamline downstream usage.

Month: 2025-09 — Focused on enabling scalable TTNN APIs and expanding operator support in the tt-mlir project. Delivered foundational constraint and runtime APIs across TTNN operations, enabling improved validation, optimization, and integration. Added GELU support in TTIR/TTKernel pipelines, and reinforced documentation and test coverage to accelerate onboarding of future ops.

August 2025 monthly summary for tenstorrent/tt-mlir focusing on delivering constraint and runtime APIs for TTNN and CNN ops, accompanied by unit tests and test-workarounds to stabilize MaxPool2dOp testing. Key outcomes include expanded operator constraint coverage, improved analysis/validation integration in the MLIR TTNN dialect, and concrete improvements to optimizer capabilities for CNN workloads. Technologies include MLIR TTNN dialect, constraint APIs, ConstantOp, RandOp, PrepareConv2dWeights, PrepareConv2dBias, AvgPool2d, BatchNorm; strong emphasis on business value: safer optimization, easier integration, and faster validation.

For July 2025, TT-MLIR work centered on strengthening the constraint API surface, accelerating build times, and advancing device-aware kernel configuration for Conv2d operations. Delivered four targeted improvements that together increase hardware portability, reduce iteration time, and improve runtime reliability across TTNN workloads.

June 2025 monthly summary for tenstorrent/tt-mlir: Delivered a unified TTNN constraint API with per-operator constraint support and runtime estimation, enabling accurate constraint retrieval for runtime planning across common TTNN operations. Refactored TTNNOpModel to use a dedicated ConstraintReturn struct, improving code maintainability and scalability. This work lays the groundwork for better planning analytics and automated resource scheduling in production TTNN workloads. No major bugs fixed this month; the focus was on feature enhancements and API robustness to support future performance optimizations.