Proudly Announcing BeetleboxCI at Embedded World 2...

We are proudly announcing BeetleboxCI at Embedded World 2021! Join us at 11:50am (GMT+1) on 3rd March to learn more at our talk!.  BeetleboxCI is the next generation of development automation for intelligent Edge computing powered by FPGAs. Gain the

Bring the power of the Cloud to devices themselves...

As demands for ever more intelligent devices increases, developers have looked towards offloading Artificial Intelligence and other data analytics to the Cloud. Sending this information has led to large increases in bandwidth and processing costs. For many applications this has

Sign Language Recognition: Hand Object detection u...

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

Getting started with Computer Vision for Vitis 202...

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

Improving Convolutional Neural Networks: The weakn...

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

Getting started with Computer Vision for Vitis 202...

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

Vitis AI using Tensorflow and Keras Tutorial Part ...

Part 9: Running our code on the DPU We now have our compiled model ready to run on our board. In this tutorial we will look at running our DPU and exploring the code that interacts with the DPU API.

Getting started with Computer Vision for Vitis 202...

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

Vitis AI using Tensorflow and Keras Tutorial Part ...

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

Vitis AI using Tensorflow and Keras Tutorial Part ...

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

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