In this tutorial, join Ari Mahpour as he explores the fascinating task of deploying neural networks on the PYNQ-Z2 FPGA board. Learn step-by-step how to train an iris classification model using the powerful hls4ml framework and validate it against both local and FPGA models.
**What You Will Learn:**
- Setting up your PYNQ-Z2 board for AI deployment
- Training and optimizing neural networks for FPGA
- Leveraging the hls4ml repository for seamless FPGA integration
- Comparing performance between CPU/GPU and FPGA models
**Resources Mentioned:**
- hls4ml Repository: https://github.com/fastmachinelearning/hls4ml
- Iris Model on FPGAs GitLab: https://gitlab.com/ai-examples/iris-model-on-fpgas/-/tree/main?ref_type=heads
For more of Ari's Neural Network Series, click here: https://www.youtube.com/playlist?list=PLtZurp7-0DcVwGxArCHvKj1yYBwU2TgQ_
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0:00 Intro
0:43 A Note before We Begin
3:17 Dataset Overview
8:04 Building the Model & Flash File
21:18 Running & Validating the Model
31:59 Wrapping Up