
During six months on the tenstorrent/tt-metal repository, Dusan Stoiljkovic engineered advanced pooling and convolution features for deep learning workloads, focusing on adaptive and 3D pooling, dynamic kernel sizing, and performance optimizations. He implemented avg_pool2d and adaptive pooling with configurable parameters, enabling flexible model architectures and precise output control. His work included refactoring kernel interfaces, improving memory management, and aligning pooling operations with PyTorch validation. Using C++, Python, and GPU programming, Dusan addressed edge cases, enhanced test infrastructure, and improved CI reliability. The depth of his contributions strengthened model correctness, maintainability, and performance for both research and production deployments.

September 2025 (2025-09) – TT-Metal: Delivered adaptive 2D pooling with dynamic kernel sizes and channel-last support, plus robust correctness and edge-case fixes. Implemented dynamic kernel sizing and stride based on output dimensions; added bindings, validations, and tests; extended support for both flattened and unflattened channel-last inputs; updated CODEOWNERS. Fixed pooling edge-cases: corrected output channel padding, rounding across shards, and partial tile handling to improve reliability. Code quality improvements and maintainability: tests, validations, and ownership updates; commits span PRs 27598, 28181, 28388, 27580, and 27832.
September 2025 (2025-09) – TT-Metal: Delivered adaptive 2D pooling with dynamic kernel sizes and channel-last support, plus robust correctness and edge-case fixes. Implemented dynamic kernel sizing and stride based on output dimensions; added bindings, validations, and tests; extended support for both flattened and unflattened channel-last inputs; updated CODEOWNERS. Fixed pooling edge-cases: corrected output channel padding, rounding across shards, and partial tile handling to improve reliability. Code quality improvements and maintainability: tests, validations, and ownership updates; commits span PRs 27598, 28181, 28388, 27580, and 27832.
Month: 2025-08 — Performance Review-Ready Monthly Summary for tenstorrent/tt-metal focusing on pooling enhancements and adaptive pooling, with hackathon exploration and robustness improvements. The work delivered broad functional improvements in pooling ops, upgraded adaptive pooling support, expanded validation, and set the stage for future optimizations. Business value centers on supporting variable input shapes, richer pooling configurations, and more reliable, PyTorch-aligned validation to reduce model risk and startup costs for deployable models.
Month: 2025-08 — Performance Review-Ready Monthly Summary for tenstorrent/tt-metal focusing on pooling enhancements and adaptive pooling, with hackathon exploration and robustness improvements. The work delivered broad functional improvements in pooling ops, upgraded adaptive pooling support, expanded validation, and set the stage for future optimizations. Business value centers on supporting variable input shapes, richer pooling configurations, and more reliable, PyTorch-aligned validation to reduce model risk and startup costs for deployable models.
July 2025 monthly summary for tenstorrent/tt-metal: Focused on benchmarking accuracy and metric governance. The ResNet50 compile-time benchmark metric was updated to 31 seconds to reflect observed performance, ensuring KPIs align with real measurements and enabling more reliable capacity planning and optimization decisions.
July 2025 monthly summary for tenstorrent/tt-metal: Focused on benchmarking accuracy and metric governance. The ResNet50 compile-time benchmark metric was updated to 31 seconds to reflect observed performance, ensuring KPIs align with real measurements and enabling more reliable capacity planning and optimization decisions.
June 2025 monthly summary for tenstorrent/tt-metal focused on delivering enhanced 2D average pooling capabilities, enabling more flexible and accurate pooling configurations for model workloads. This work improves model correctness and user control over output shapes and values, supporting research and production deployments that rely on nuanced pooling behavior.
June 2025 monthly summary for tenstorrent/tt-metal focused on delivering enhanced 2D average pooling capabilities, enabling more flexible and accurate pooling configurations for model workloads. This work improves model correctness and user control over output shapes and values, supporting research and production deployments that rely on nuanced pooling behavior.
April 2025: Implemented avg_pool2d in the ttnn API, refined CI and test infrastructure for pooling features, enabled blackhole tests in nightly CI, and delivered kernel-level pooling performance and robustness improvements. These changes broaden model design options, improve test reliability, and boost runtime performance.
April 2025: Implemented avg_pool2d in the ttnn API, refined CI and test infrastructure for pooling features, enabled blackhole tests in nightly CI, and delivered kernel-level pooling performance and robustness improvements. These changes broaden model design options, improve test reliability, and boost runtime performance.
March 2025 monthly summary for tenstorrent/tt-metal focused on stabilizing and accelerating core Conv2d/Conv3d/Pool workloads through circular buffer indexing improvements. Implemented dynamic, sequential assignment of circular buffer indices across Conv2d/Conv3d/Pool program factories, reducing warnings, improving dispatch performance, and enhancing maintainability. Introduced common utilities for Conv2d program factories, refactoring to improve linting, and ensured buffers are contiguous where beneficial. Adjusted kernel interfaces to pass index values as compile-time arguments where needed to ensure synchronization and avoid hangs, yielding more predictable runtimes. Completed targeted clang-tidy fixes and include hygiene to reduce build noise and improve CI reliability.
March 2025 monthly summary for tenstorrent/tt-metal focused on stabilizing and accelerating core Conv2d/Conv3d/Pool workloads through circular buffer indexing improvements. Implemented dynamic, sequential assignment of circular buffer indices across Conv2d/Conv3d/Pool program factories, reducing warnings, improving dispatch performance, and enhancing maintainability. Introduced common utilities for Conv2d program factories, refactoring to improve linting, and ensured buffers are contiguous where beneficial. Adjusted kernel interfaces to pass index values as compile-time arguments where needed to ensure synchronization and avoid hangs, yielding more predictable runtimes. Completed targeted clang-tidy fixes and include hygiene to reduce build noise and improve CI reliability.
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