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
Introduction In our previous tutorial series, we looked at sign language recognition using the sign language MNIST dataset based off the original 1999 MNIST dataset, which is considered the “Hello World” of machine learning. We did this because we wanted
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 8: Compiling our CNN We now have our complete model and must make it ready to be run on the FPGA. To do this, we must compile our model with the Vitis AI compiler which will convert and optimise
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 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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