EDGE AI ON ZYNQ ULTRASCALE+: VITIS AI, KRIA SOM, AND FPGA INFERENCE: Quantize and Deploy TensorFlow and PyTorch Models Using FINN, DPU, and AXI Accelerators Without RTL Knowledge

Build practical edge AI inference pipelines on Zynq UltraScale+ using Vitis AI, Kria SOM, DPU, FINN, and AXI accelerators without writing RTL by hand.Deploying AI models on FPGA-based edge devices is not just about training a neural network. You must match the toolchain, quantize the model correctly, compile for the right DPU target, prepare the board, manage runtime files, move data efficiently, and debug accuracy or performance problems when the deployed result does not behave like the desktop model.Edge AI on Zynq UltraScale+: Vitis AI, Kria SOM, and FPGA Inference gives you a clear, developer-focused path through the full deployment stack, from TensorFlow and PyTorch model preparation to DPU execution, Kria board workflows, low-precision FINN acceleration, Vitis HLS kernels, and production-style debugging.Inside, you will learn how to:Understand how the Zynq UltraScale+ processing system and programmable logic work together in an edge AI pipelineMatch Vitis AI, Vivado, PetaLinux, XRT, VART, board images, DPU IP, arch.json files, and xmodel artifactsPrepare TensorFlow, Keras, and PyTorch models for quantization, export, compilation, and deploymentBuild representative calibration datasets and apply post-training quantization or quantization-aware trainingCompile quantized models for the correct DPU target and run inference with VART Python and C++ workflowsPrepare Kria boards for AI acceleration, load accelerated applications, check DPU status, and manage deployment filesCreate computer vision pipelines for image classification, object detection, preprocessing, postprocessing, VVAS, and GStreamerUse Vitis HLS to build AXI4-Lite, AXI4-Stream, and memory-mapped AXI accelerators for resize, color conversion, normalization, and tensor movementDecide what belongs on the CPU, DPU, FINN accelerator, or custom HLS kernel based on accuracy, latency, data movement, and maintainabilityTrain low-precision quantized neural networks with Brevitas and prepare QNN models for FINN dataflow accelerationDebug wrong accuracy after quantization or board deployment by checking preprocessing, tensor layout, output scaling, model files, and runtime compatibilityProfile DPU time, CPU time, memory movement, FPS, latency, dropped frames, power, and thermal behaviorPackage models, firmware, application files, configuration, logs, health checks, and rollback paths for repeatable deploymentThis is a code-heavy technical guide with practical Python, C++, Shell, YAML, CMake, and HLS-style examples that show how real deployment workflows are structured across model preparation, board verification, inference, acceleration, logging, profiling, and recovery.If you want a grounded guide to deploying edge AI models on Zynq UltraScale+ and Kria platforms while understanding the full stack behind DPU, FINN, Vitis HLS, and AXI-based acceleration, this book gives you the workflow and context to build with confidence.Grab your copy today and start building practical FPGA-powered edge AI inference pipelines. Read more