Hi everyone, welcome to the video. This video will introduce the one of the first hardware demonstration design of the Statue of Machine on Iris dataset. I will briefly explain Iris Flower Machine Learning Forum and hardware architecture of my design to you. In addition, a video demo will follow. There is a type of a flower called Iris. It can withstand different shapes. A scientist has discovered if you measure the width and length of the certain area of the flowers. We can cut it blinded in three major groups. Therefore, we can use this special pattern to train our machine learning algorithm. Because the unique feature of Statue of Machine, the training data need to be fed in to the system in binary. So we need to banalize the features and the classes of the live data. The banalized feature and classes can be called literals and why or why expected in my design. This design contains two main devices. A microcontroller that is able to send data set and receive learning outcome from the Statue of Machine. A FPGA which contains the Statue of Machine that is able to generate learning outcome based on the receiving data. Microcontroller and FPGA can communicate with each other through a protocol. Microcontroller will send literals to the certain machine and such machine will return back the learning outcome. In order to make this process easier to understand, a display and a switch had attached to increase the user friendliness of the device. As you can see in the display, you can choose to train or test the Statue of Machine by selecting the mode in the GUI. If you choose to train a system, the microcontroller will send 16 bits information containing the features of the iris flower and two bits of expected learning outcome which are literals and why expected to show in the GUI. When the Statue of Machine receives the training data, it will compute the learning outcome Y. The data sent out and received in the microcontroller will display it in the screen in real time so that the learning accuracy can be seen. As you can see, the learning accuracy is relatively low but after few training cycles, the learning accuracy increases gradually. Let's fast forward to see the final accuracy of the Statue of Machine. As you can see, the Statue of Machine can achieve around 19.5% accuracy in the end. You may ask, the 90% accuracy is not high at all. Well, the FPGA will give you a quick overview of the Statue of Machine. As we can see, the Statue of Machine can achieve around 19.5% accuracy in the end. You may ask, the 90% accuracy is not high at all. Well, the FPGA will use is a low end product that can only fit certain specs of the Statue of Machine and this design is aiming to demonstrate technology rather than benchmarking. Now we can start a testing of the Statue of Machine. After few testing cycles, the average accuracy of the Statue of Machine can achieve is around 89% which is close to our expectation. Though thank you for your watching, great thanks to the fellow researchers at Newcastle University. If you want to learn more about Statue of Machine, please visit this to GitHub pages. I look forward to seeing you again. You