Tamar S.

Responsibilities:

• Executed systematic experiments to evaluate model performance using Batch Size, nstreams, nireq, and CPU pinning. Compared Precision Modes (FP32, FP16, INT8, Mixed Precision) with quantization and post-training optimization. Measured and visualized key metrics such as Latency, FPS, and Memory Footprint, generating histograms and comparative graphs to analyze trade-offs between accuracy and efficiency.https://github.com/TamiShaks-2

• Researched Intel OpenVINO architecture (Frontend, Core, Runtime) and neural network computation flow. Gained hands-on understanding of graph representation (IR), model conversion, and optimization pipelines from PyTorch to OpenVINO.

• Processed the COCO dataset, performed format conversion (COCO → YOLOv9), and created customized YAML configuration files for experiments. Executed YOLOv9-Seg and EfficientNetV2 models using OpenVINO Runtime, analyzed Latency and Throughput metrics, generated histograms and performance graphs, and optimized inference efficiency across different hardware configurations.

• Implemented the linalgmultidot operator in the OpenVINO PyTorch Frontend using Matrix Chain Optimization (MCO). Achieved ~35% reduction in computation cost for multi-matrix operations. Ensured support for dynamic shapes, 1D edge cases, and consistent dtype handling.

• Developed and ran automated PyTest test suites to validate the correctness, stability, and performance of the linalgmultidot operator. Designed tests for various matrix shapes, heuristics, and dynamic dimensions to ensure numerical equivalence between PyTorch and OpenVINO outputs. Used assertions and graph-structure validation to verify optimal MatMul order and sub-product formation.

• Configured multi-layer debugging using VS Code + GDB for cross-language tracing between Python and C++. Monitored operator calls through PyBind11, managed breakpoints across OpenVINO’s frontend and core layers, and created a custom launch.json for reproducible debugging sessions.