Esther Z.

Responsibilities:

• Conducted research on quantization techniques for advanced vision models (ResNet-50, EfficientNet-XL, YOLOv8), including FP32→INT8/FP16 conversions and developing mixed-precision methods to enhance computational efficiency.

• Developed a benchmarking infrastructure for CPU/GPU execution, measuring latency and throughput and tuning batch size, nstreams, and nireq to optimize real-time inference performance.

• Gained deep understanding of OpenVINO’s architecture—Model Optimizer, IE Core, Frontends, Runtime, and the Intermediate Representation (IR)—and its role within the model pipeline.

• Implemented new PyTorch Frontend operators such as fakequantizelearnable and SmoothL1Loss, performed numerical validation and unit testing, and submitted an official PR.

• Performed operator lowering and translated custom operators into ov::Model, ensuring precise shape inference and stable integration across the PyTorch→OpenVINO execution flow.

• Authored an official Debug Guide documenting Source Build setup, configuration workflows, and VS Code integration, establishing standardized debugging practices for the development team.

• Executed cross-language debugging between Python and C++ using pybind11, analyzing runtime issues, signal handling, and attach/launch debugging scenarios to improve system stability.