Part 1: Introduction Welcome to our multi part tutorial on using Vitis AI with TensorFlow, Keras and BeetleboxCI. This tutorial series is designed to teach the entire development process from initial code to forming an entire Continuous Integration pipeline that
FPGA design has long been using traditional hardware development practises such as waterfall and V-model, but we believe the future of FPGAs lies in continuous integration and in this video we explore why. Transcript: FPGA design development has always embraced
For our introduction to neural networks on FPGAs, we used a variation on the MNIST dataset made for sign language recognition. It keeps the same 28×28 greyscale image style used by the MNIST dataset released in 1999. As we noted
Welcome to the 2020.1 version of getting started with computer vision on Vitis on Zynq. The release of 2020.1 saw significant changes from the old 2019.2 version and we thought it would be useful to update this tutorial to reflect
Welcome to the 2020.1 version of getting started with computer vision on Vitis on Zynq. The release of 2020.1 saw significant changes from the old 2019.2 version and we thought it would be useful to update this tutorial to reflect
Part 7: Quantising our graph In our previous tutorial we produced our frozen model so now we can optimise it to make it run on our FPGA hardware efficiently, which we can do through quantisation. Quantisation is the process of
Part 6: Converting and Freezing our CNN Now we have built a more optimal CNN by handling both under-fitting and over-fitting, we can begin the process of deploying our model on the FPGA itself. The first step in this process
Part 5: Optimising our CNN In our previous section, we both trained our network on a training set and tested it on a testing set and our accuracy on the training set (0.972) was higher than on our testing set
Part 4: Training the neural network Welcome to Part 4 of our tutorial where we will be focused on training the neural network we built in the previous section Introduction Getting Started Transforming Kaggle Data and Convolutional Neural Networks (CNNs)
Part 3: Extracting Kaggle data and building the Convolutional Neural Network (CNN) Welcome to Part 3 of our tutorial where we will be focused on how to extract our data from the Kaggle set and building our Convolutional Neural Network.
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