In January 2026, delivered a targeted optimization in the ResNet inference pipeline for tenstorrent/tt-forge by refactoring the output post-processing logic to streamline handling of inference results and addressing a correctness issue. The changes improve reliability, maintainability, and production readiness for the inference pipeline, enabling smoother downstream processing and faster iteration on model deployments.

February 2025 (2025-02) monthly summary for tenstorrent/tt-tvm. Key features delivered: - Forge: Execution phase and stage tracking in the compilation pipeline. Implemented instrumentation to record execution phase and stage at key points across the Forge pipeline, including PyTorch, ONNX, TFLite, TensorFlow frontends and Forge-specific passes, enabling granular tracing of the compilation workflow. Major bugs fixed: - None documented this month. Overall impact and accomplishments: - Provides end-to-end visibility of the compilation process, facilitating faster debugging, root-cause analysis, and targeted performance optimizations. The instrumentation lays the groundwork for data-driven improvements across the frontend integrations and Forge passes. Technologies/skills demonstrated: - Instrumentation and telemetry integration, cross-frontend pipeline tracing, commit traceability, and collaboration across frontend and Forge components.