Kickstart AI automation on FPGAs with BeetleboxCI

Check out our new short presentation on how BeetleboxCI provides whole application acceleration to AI, keeping with innovation far faster than other silicon chips: The speed of AI innovation is outpacing silicon chips. FPGA Accelerators are the solutions. Here

The easy getting started guide with BeetleboxCI an...

Part 2: Quick Start Guide This is a quick start tutorial designed to show how to get started using BeetleboxCI and Vitis AI, in which we will cover the following: Designing a Neural Network – Designing a simple neural network

Complete Vitis AI tutorial with TensorFlow, Keras ...

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

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

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

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.

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

Vitis AI using Tensorflow and Keras Tutorial part ...

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

Vitis AI using Tensorflow and Keras Tutorial Part ...

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

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