June 2026 performance summary across three repos (tt-forge-models, tt-xla, tt-mlir) focused on stability, regression coverage, and runtime simplification. Delivered targeted changes to guard against upstream drift, stabilize test environments, and expand regression testing, enabling faster, safer feature delivery and fewer nightly failures.

Month: 2026-05 — Focused on delivering robust TT-MLIR backend changes that improve correctness, portability, and maintainability, enabling smoother uplift to XLA and more reliable PCC testing. Key deliverables and impact: - Key features delivered: - TTIR builder reliability improvements: rewritten gelu builder to use decorator-based parsing and triplet pattern, aligning with existing ops; fixes to tt nn layer norm and update_cache semantics to ensure consistency with the rest of the stack. - Mesh normalization unification: centralized 0D/1D/2D mesh normalization in normalizeMeshTo2D, removing duplicated logic; standardized axis naming to _aux; tests updated to reflect the unified approach. - Major bugs fixed: - Update_cache_golden now matches TTNN update_cache semantics (batch_offset handling, per-slot update_index behavior). - ttnn_layer_norm_golden now applies float casting to weight and bias when present, preventing CPU FP32/ bf16 mix issues. - Reverted all_reduce decomposition workaround to restore coherent model outputs after test regressions (cross-repo uplift stability). - Overall impact and accomplishments: - Improved correctness and stability of critical data-paths, reducing flaky tests and mismatches when uplifting to TT-XLA. - Reduced duplication and improved maintainability through normalization unification and standardized axis naming. - Strengthened test coverage to validate the updated behavior across 0D/1D/2D meshes and TTIR builder components. - Technologies/skills demonstrated: - Python decorator-based redesign and code refactoring for TTIR builder components. - Backend normalization logic consolidation in C++/Python glue layers; axis naming conventions. - Test-driven development with updated test expectations across multiple subsystems; clear commit hygiene and traceability (e.g., commit references in notes).

April 2026 monthly summary: Expanded TTIR capabilities, reduced maintenance overhead, and improved CI reliability across tt-mlir and tt-xla. Delivered 5D Max Pooling support in TTIR reduce_window by decomposing a 5D input into two sequential 2D passes, enabling 3D pooling workflows. Consolidated TTIR Gather into StableHLO Gather, migrating tests and deprecating TTIR Gather to a StableHLO-based path, reducing duplication and maintenance. On the TT-XLA side, tuned nightly tests for stability and model inference, including skipping a hanging qwen test, adjusting activity and performance criteria, lowering PCC for owl_vit during investigation, and removing an inappropriate nightly marker to align with current capabilities. These changes broaden functionality, improve reliability, and accelerate model deployment cycles.

March 2026 performance summary focusing on key features delivered, major bugs fixed, overall impact, and technologies demonstrated across tt-mlir and tt-xla.

February 2026 monthly summary across tenstorrent/tt-mlir and tenstorrent/tt-xla. Focused on delivering automated error traceability, improving debugging workflows, and stabilizing CI. Key outcomes include automated capture of IR modules from failed operations, an enhanced op-by-op tooling CLI for saving failure artifacts, and a suite of CI/test configuration fixes that reduced flakiness in nightly runs. Demonstrated strong cross-repo collaboration, scripting/automation, and emphasis on business value through faster issue diagnosis, clearer artifact pipelines, and more reliable software delivery.

January 2026 performance summary for tt-mlir and tt-xla. Focused on strengthening TT-XLA integration and expanding modular testing capabilities. Delivered op-by-op infrastructure enhancements, robust MLIRModuleSplitter handling for ttcore.load_cached and TTNN ops, test updates, and an automation script to validate SHLO IR graphs across multiple models. These efforts reduced per-op validation time, improved execution reliability, and enabled scalable testing workflows across both repositories.

December 2025 monthly summary for Tenstorrent development efforts across tt-mlir and tt-xla. Focused on stabilizing core conversion paths, expanding testing capabilities, and strengthening CI/test visibility to deliver faster, more reliable model iteration cycles.

November 2025 monthly summary focused on delivering business value through stable integration, robust testing, and flexible data handling across core repos tt-mlir and tt-xla. Highlights include enhanced StableHLO support, broader TTIR indexing generality, and a stabilized test framework to improve reliability and observability for model evaluation.

In 2025-10, delivered a PJRT Implementation Refactor in tt-xla, expanding maintainability and clarity of the PJRT API by removing legacy code and reorganizing structure; in tt-mlir, implemented robust TTIR scatter validation (checkBasicLegality) to replace crashes with failure statuses, and extended TTIR-to-TTIR decomposition to support complex gather indexing for embedding (AlexNet-compatible) scenarios. Overall, these changes improve stability, model compatibility, and maintainability, reducing risk for production deployments and accelerating future feature work. Key technologies: C++, PJRT API refactor, error handling, embedding ops, TTIR decomposition.

Month: 2025-09 – Summary of developer contributions across the repository portfolio Key features delivered - tt-xla: Implemented ModelInfo-based test data recording and reporting. Introduced new properties model_info and parallelism while preserving backward compatibility with model_name and model_group. Refactored Bert test case to adopt the new ModelInfo structure. Commit e7fe37266d2d85a68ee23c50303c43b3f5ea2d08; message: 'Using ModelInfo to record test properties (#1037)'. Business value: richer test metadata, improved reporting consistency, and easier test result traceability. Major bugs fixed - tt-mlir: TTNN Embedding Input Shape Compatibility fix. Reshaped startIndices to 2D {1,N} to satisfy ttnn.embedding input requirements, preventing 1D input errors and ensuring compatibility with ttnn.embedding operation. Commit 5069bf89c306bd2e097d6124008dcc5b120aa1f9; message includes the ticket and rationale. Business value: stabilized embedding transformations, reducing flaky test failures and aligning with MLIR/ttnn expectations. Overall impact and accomplishments - Strengthened test data governance and reporting across modules by adopting ModelInfo, enabling better visibility into test scenarios and resource usage. Resolved an embedding input shape issue that previously blocked certain test paths, improving overall test suite reliability and throughput. These changes position the team for smoother model testing cycles and easier future enhancements. Technologies/skills demonstrated - Data modeling and API evolution with ModelInfo; backward-compatible changes and test refactoring. MLIR/TTNN embedding transformations and tensor reshaping. Test automation discipline, cross-repo collaboration signals, and attention to compatibility with existing test suites.

August 2025: Delivered dynamic slice support and StableHLO conversion for the tt-mlir/ttnn dialects in tenstorrent/tt-mlir. Implemented dynamic slice operation, renamed the static slice for consistency, and added conversion logic from StableHLO to handle dynamic slicing. Updated tests and implemented workarounds to ensure slice operations are robust across dialects. This work establishes runtime-flexible slicing for model porting and improves cross-dialect interoperability.

In July 2025, delivered a focused enhancement to the Gather Operator test suite in tenstorrent/tt-xla to improve reliability across diverse shapes and large inputs. Implemented broader input shape support and higher test input values, added protections to prevent indices tensors from reducing to all zeros, and extended test coverage to align with real-world model scenarios. The change reduces regression risk for gather-related models and accelerates CI feedback.

June 2025 summary for tenstorrent/tt-metal: Delivered robust improvements to the async tile processing and distributed data movement stack, focusing on correctness, performance, and scalability. Key outcomes include remediation of tile-based packet_id calculation when tile counts are not evenly divisible, enhancements to all-gather data flow with tensor padding, and tile granularity/reduce-scatter optimizations to reduce memory usage and latency. Added/updated tests to validate padding and data movement, strengthening production reliability. These changes collectively advance the pipeline robustness and scalability for distributed workloads.

May 2025 monthly summary for tenstorrent/tt-metal focusing on feature deliveries, performance optimizations, and stability improvements. Key scope included high-impact data-transfer optimizations for bfloat8, scatter-reduction performance tunings, and reliability fixes in tile-based asynchronous I/O. The work demonstrates practical optimization across memory access patterns, compile-time configurability, and kernel-level correctness, delivering measurable business value in bandwidth efficiency and compute throughput.

Month: 2025-04 Concise monthly summary focusing on key accomplishments, business impact, and technical achievements for the tt-mlir repository. Overview: Delivered targeted improvements to the TTIR lowering path to enrich embedding support, enabling models with gather-based embeddings and expanding the set of viable workloads. This work enhances maintainability and sets the stage for broader model compatibility across TTIR/TIR pipelines.

March 2025 performance summary focused on delivering scalable multi-chip execution, centralized executable output metadata, and expanded TTIR convolution support. Achieved end-to-end improvements across two repositories (tt-xla and tt-mlir) with direct business value: reduced manual configuration, better profiling/monitoring, and broader hardware compatibility.

February 2025 monthly summary for tenstorrent/tt-xla focusing on delivering reliable execution correctness, improved device identification, and codebase maintainability. Highlights include a critical bug fix ensuring scalar output status is correctly associated with ExecutableImage, dynamic device descriptions for accurate hardware identification across diverse platforms, and removal of redundant PJRT API bindings to reduce maintenance overhead.

January 2025 monthly summary for tenstorrent/tt-xla focused on API upgrade readiness and foundation work. Delivered PJRT C API v0.60 upgrade with new buffer types and asynchronous host-to-device transfer capabilities via stubs, establishing compatibility groundwork for future performance enhancements. No major bugs fixed this month.

Period: December 2024. Delivered TOSA to TTIR conversion support for core tensor ops in tenstorrent/tt-mlir, expanding TTIR coverage and enabling downstream optimizations. Implemented conversion patterns for element-wise unary, binary, ternary, logical, and comparison ops, plus dedicated patterns for ClampOp and ConcatOp. Added tests to validate the conversion pipeline. This work improves interoperability between TOSA and TTIR, enabling more robust model deployments and maintainability.

November 2024 monthly summary for tenstorrent/tt-mlir focused on delivering a high-impact refactor in the TOSA to TTIR conversion pathway, with code quality improvements and clearer architecture to support faster future iterations.