Month: 2026-05 Summary of work on aobolensk/openvino focused on architecture-aware NPU tiling to improve cross-generation performance and test coverage. Key achievements: - Implemented Architecture-aware NPU Tile Configuration with per-generation rules and tests (commit 7dcd68cc424d9bc882de5225979ee1109c36f9dc). Added ArchAwarePlugin test helper to simulate different NPU architectures. - Developed per-generation NPU_TILE policy: - prior to 4000 series: do not alter NPU_TILES - 4000 series: set NPU_TILES to max_tiles and optimisation_level=3 - 5000 series: set NPU_TILES to max_tiles - AUTO_DETECT: latency-optimized compilation without explicit tile settings - Expanded test coverage with unit tests for different NPU generations. Local unit tests pass; validation CI for 4000/5000 series hardware is planned. Major bugs fixed: - None reported in this work item. Overall impact and accomplishments: - Enables architecture-aware NPU tiling across generations, delivering potential latency reductions for LLM inference and reducing manual tuning. Improves maintainability by centralizing per-generation optimization logic and strengthening test coverage, contributing to CI readiness and cross-gen support. Technologies/skills demonstrated: - Architecture-aware optimization, test-driven development, cross-generation hardware support, and test helper utilities (ArchAwarePlugin). Performance validation planning and CI-readiness.

April 2026: Delivered quantization-driven KV-cache enhancements across two OpenVINO repos, improving inference speed, model precision compatibility, and pipeline robustness. Highlights include a dynamically quantized INT-8 KV-cache framework with configurable precision in aobolensk/openvino and Whisper KV-cache suffix recognition improvements in openvinotoolkit/openvino. These changes strengthen performance, cross-version compatibility, and CI validation across Whisper and KV-cache pipelines.

Month: 2026-03. Focused on enhancing matrix multiplication reliability and performance in the openvino repo. Delivered Matrix Multiplication Tail-Constant Preservation Improvements by introducing new gemma2 patterns to preserve tail constants, improving performance and accuracy for specific compiler versions. Addressed earlier accuracy issues in gemma2-asym; patch behavior is restricted to compiler version 7.28 to ensure stability. Work tracked under E-189635 with commits 726fdafd54882d4ac4da7c9c0b51109ad1814480, co-authored by Dmitry Matveev. Impact: stronger inference performance on NPU backends and more predictable behavior across compiler versions, contributing to product reliability and customer trust.

February 2026 (openvinotoolkit/openvino): Delivered KV-Cache Inference Optimization with FP8 precision by integrating LPT passes to decompose the FakeConvert layer and retain KV-cache in FP8, aimed at boosting inference throughput and efficiency. This work aligns with ticket E-186663 and was co-authored by Ekaterina Shiryaeva and Dmitry Matveev. While performance regressions are expected to be addressed in upcoming work, the change establishes groundwork for lower memory bandwidth and reduced latency in KV-cache-enabled inference paths.

December 2025 monthly summary focused on delivering features that broaden model compatibility, improve maintainability, and ensure integration stability for the NPUW-enabled OpenVINO stack.

2025-09 Monthly Summary: Delivered critical bug fixes and stability improvements across two OpenVINO repositories (aobolensk/openvino and openvinotoolkit/openvino). Key outcomes include enabling AVX2 optimizations for NPU unit tests and stabilizing permute operations for int4 multi-channel data, accompanied by related build/compile improvements. These changes enhance test reliability, model inference stability, and potential performance gains, while demonstrating strong cross-repo collaboration and solid CMake/MSBuild proficiency.

August 2025: Delivered RoPE optimization enhancements and sanitizer-friendly unit test fixes for aobolensk/openvino, focusing on performance, accuracy, and build reliability in NPU contexts. Key outcomes include LUT-based FP32 precomputation of ROPE sine/cosine operations, conditional RoPE caching in the NPUW LLM compiled model to apply caching only when it improves accuracy per performance hints and prompt length, and a sanitizer-enabled NPU unit-test build fix by limiting included sources to essential .cpp files.

June 2025 monthly summary for aobolensk/openvino focusing on SDPA path improvements and stability.

April 2025: Implemented per-request prompt lookup performance metrics in the continuous batching pipeline of openvino.genai, enabling granular latency measurement and optimization opportunities for the text generation workflow. The instrumentation enhances observability and informs performance-driven improvements.

March 2025: NPU optimization work focused on Transpose V-tensors with test coverage and CI updates. Refactored the optimization logic to correctly handle parameter transpositions and concatenation axes, added unit tests, and extended CI workflow to cover new test cases and ensure the optimization pass is detected for Llama2 and Llama3 subgraph patterns. This was backed by a commit that adds unit tests for transpose vtensors optimisation, enabling earlier regression detection and more reliable releases.

February 2025: Delivered targeted optimization for LLM v-tensor handling in aobolensk/openvino, focusing on Llama 3-like architectures. Refactored TransposeValueTensors to correctly handle broadcasting and reshaping, enabling more efficient multiplication with attention scores. Implemented minor logging and configuration updates within the Intel NPU plugin. This work paves the way for higher inference throughput and lower latency on transformer-based models.

December 2024 (2024-12) monthly summary for openvinotoolkit/openvino.genai: Delivered a targeted optimization feature for NPU workflows within StaticLLMPipeline under FAST_COMPILE builds. Implemented NPUW_UNFOLD_IREQS config to enable NPU unfold optimizations, updated string/hint logic to respect FAST_COMPILE and BEST_PERF, and adjusted get_default_generate_config to auto-enable the NPUW_UNFOLD_IREQS when FAST_COMPILE is active. Commit: e2fa0d002117bbfe6d9a6a4cc413e583c4455131 (#1275). Business value: faster build/deploy cycles for NPUs, improved accuracy of hints, and better alignment of compilation with hardware-specific optimization.