UNET FPGA Acceleration Affiliation: University of Ostrava, Faculty of Medicine This solution is designed for the diagnostic analysis of Parkinson’s Disease using transcranial ultrasound imaging. The pipeline operates in two primary stages: Brain Stem Localization: The first deep neural network (U-Net) processes the raw ultrasound data to identify and segment the brain stem mask. Substantia Nigra Identification: This segmented region is then used as the Region of Interest (ROI) for a second specialized network. Its goal is to identify the Substantia Nigra (SN)—the primary focus of this research, as its echogenicity is a key biomarker for Parkinson’s. Final Output: Once the objective is identified, the system automatically fits an elliptical regressor around the Substantia Nigra. Targeted devices Kria KV260 Pynq Z2 MYIR FZ3 MYIR FZ5 Key capabilities Training: Integration with Keras and Vitis AI Quantizer to prepare U-Net models for low-precision FPGA inference. The training flow is highly configurable, allowing for quick changes in the neural network and training parameters. Vitis AI Flow: Full implementation of the Vitis AI compilation chain, transforming high-level models into hardware-optimized .xmodel files. DPU Acceleration: Leveraging the Xilinx DPU for high-throughput, low-latency image segmentation. Webapp: Includes a ready-to-use application for real-time inference and demonstration on the Kria KV260. The application can also be run on the Pynq Z2, MYIR FZ3, and FZ5 boards. Performance Benchmarking: Tools to evaluate inference accuracy and latency directly on the target hardware. Software/Hardware Comparison: Both models are implemented in software flow and hardware-accelerated flow for direct comparison, and are available for use in the included web application. Ellipse regresor: The second part of the AI flow is implemented as a UNET model and a small ellipse regresor. Which will be used for further study of the echogenicity, Features Target Hardware: Specifically optimized for Xilinx Kria KV260, Pynq Z2, MYIR FZ3 and FZ5 End-to-End Pipeline: Covers everything from data preparation and training to quantization, compilation, and final deployment. Hardware-Specific Scripts: Includes automated shell scripts for the entire Vitis AI flow (quantization, evaluation, and compilation). Webapp: A complete web-based UI for interacting with the deployed model on the board. What's included Vitis AI Source: Comprehensive scripts (vitis_ai/) for the DPU compilation flow (scripts 0_... through 5_...). Training Logic: Quantized U-Net implementation and training scripts compatible with FPGA deployment requirements. Deployment Tools: The webapp source code and helper scripts for board-side execution. Minimum requirements OS: Desktop Linux (Ubuntu 18.04/20.04 recommended for Vitis AI tools). Runtime: Python 3.7+; Vitis AI Docker environment or local toolchain (Quantizer, Compiler). Hardware: * Development: Workstation with Xilinx Vitis AI toolchain installed. Target: Xilinx Kria KV260, Pynq Z2, MYIR FZ3, MYIR FZ5 with DPU-based image. Data: Raw images for segmentation How to cite Cite the Zenodo DOI for this version. Authors: Denis Kurka, Petr Čermák