vitis-ai

Workflow for Executing CNN Networks on Zynq Ultrascale+ with VITIS AI. Detailed analysis, configuration, and execution of Convolutional Neural Networks on ZCU102 using VITIS AI, evaluating performance on the board compared to Cloud infrastructure. Developed for educational exam purposes.

Hardware Accelerators on FPGA for Computer Vision Applications

PYNQ enabled Genesys ZU Open Source Instrument, based on Pavel Demin's Red Pitaya Notes workflow.

Quantize, compile and run a pretrained Pytorch model on a Xilinx KV260 to control a FireTV using hand gestures from USB camera input

End-to-end pipeline for ultrasound image reconstruction using DAS, deep learning, and FPGA deployment (Kria KV260)

Custom YoloV4 Darknet/Tensorflow model for license plate detection on the AMD-Xilinx Kria KV260 Vision-AI starter Kit. Utilize transfer learning to create your own custom object detecion model on a custom dataset, quantize and compile in Vitis-AI for easy deployment and evaluation on FPGA.

Quantization of efficientnet model that i pruned 81% using Vitis AI and compiling it for deployment on FPAG

"Vitis-AI-YOLOv3-TF2-Quantization-Evaluation" Repo for quantization of YOLOv3 on Vitis-AI using TF2, aimed to deploy model on edge devices with limited resources. Includes training & quantization scripts and evaluation metrics. Experiment with different configurations.

Xilinx DPU(Vitis AI)を用いたエッジAI実現に向けたサンプルプログラム

Human falls is a major reason for deaths in elderly people. It can be prevented by an automatic fall detection, and alert system. We have created an automatic fall detection algorithm running on the Xilinx ZCU104 FPGA.

End-to-end pipeline for optoacoustic image reconstruction using DAS, deep learning, and FPGA deployment (Kria KV260)

Compression Supports Spatial Deep Learning

This repository serves as a medium for universal sourcing to a compiled (using vitis ai) resnet50 neural network. I'll be adding more stuff for testing as the summer goes on.

Deploys quantized ResNet18 on ZCU104 FPGA with Vitis AI, focusing on accuracy and efficiency.

The Flow to Deploy your Custom Deep Learning Models on Ultra96V2.

A Convolutional Neural Network (CNN)-based model trained to detect kidney cancer from histopathological images and deployed onto the AMD Kintex-7 FPGA board using the hls4ml and Vitis AI.

A full Mamba (SSM) block on the Xilinx Alveo U55N in Vitis HLS, verified bit-exact against the PyTorch reference

Vitis AI tutorial for MNIST and CIFAR10 classification

Experiments, notes, and benchmarks for AMD Ryzen AI (Krackan Point NPU + Radeon iGPU) on Linux

Agent pipeline that extracts a deep learning model operations, checks them against Vitis AI docs with RAG, and proposes a compatible architecture