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Two major challenges of embedded artificial intelligence
Limited computing resources. Nowadays, artificial intelligence requires deep learning for visual processing, speech recognition and natural language processing. The neural network parameters are generally large, and the network with several hundred layers is not a minority. It needs to consume a lot of memory. How to optimize the network data without losing the data precision is one of the important challenges of embedded artificial intelligence. 
Limited computing power. The computing power of the embedded platform can not be compared with the computing power of traditional PCs, such as the Convolutional Neural Network (CNN) commonly used in deep learning, which can solve most of the problems of image processing. However, the calculation time of the convolution part accounts for more than 70% of all calculation time. So how to optimize computing power is also an urgent problem to be solved.
How to deal with the challenges of embedded artificial intelligence? NVIDIA gave the answer. In some recent internationally renowned hard-core activities, we can see many wonderful AI robots and drone cases, all using deep learning technology. And they all have one thing in common - NVIDIA Jetson TX1 
The Jetson TX1 is the NVIDIA second-generation embedded platform developer suite. Although it is only credit card size, the Jetson TX1 GPU module has a floating point capability of 1 Teraflops. This AI supercomputer uses NVIDIA MaxwellTM architecture, 256 NVIDIA CUDA® cores and 64-bit CPU, 4GB LPDDR4 RAM memory (25.6Gbps bandwidth per second), 16GB local storage module, 802.11 2×2 ac Wi-Fi solution The solution and 1Gbps Ethernet port are also equipped with the Jetson TX1 Developer Suite for Visual Computing.
Because the CUDA and cuDNN interfaces are fully supported, it is easy to port the deep neural network trained on the PC to the embedded platform of the Tegra X1. In addition, TX1 can also provide a rich hardware interface for easy access to a variety of hardware devices, including cameras, sensors and so on. NVIDIA also provides a powerful software toolkit for Jetson, as shown in the following figure: 
NVIDIA Jetson TX1 application example
Dr. Zhao Kaiyong from the Computer Department of Hong Kong Baptist University based on Jetson TX1 made some performance optimizations for convolution calculations, including running CPU, GPU and memory at the highest frequency to achieve coordinated scheduling of CPU, GPU and memory. By optimizing the parallel computing of the GPU, the purpose of space change time is achieved.
A feature of NVIDIA's GPU memory access, continuous merge access, makes good use of hardware bandwidth. From the architecture point of view, optimizing IO access, even if the number of cores does not increase much, the architecture does not change much, just increase the cache in the middle of several computing stream processors, can improve the performance. The figure below is a classic GPU memory architecture and threading model. 
Intelligent Flying Machines (IFM) is a data analytics company that uses computer vision and robotics to automate warehouse data capture.
NVIDIA TrailNet Deep Neural Network's Iris+ drone, with the help of Computer Vision and Deep Learning technology, smoothly crosses the forest trail.
JetsonTX1 has become the most dazzling equipment in the eyes of countless hardware makers. At present, TX2 has also been launched. In addition to supporting all the functions of the Jetson TX1 module, it can also support larger and more complex deep neural network calculations, which will definitely make hardware makers shine. 
Text editor: Zi Caijun
Picture video: Network
Jetson UAV Embedded Artificial Intelligence Platform
The geeks' pursuit of drones is endless. In addition to the requirement for light weight and long battery life, it is more important to have a smart "brain". And this smart brain is best to have excellent performance for deep learning, as well as a physical volume that is lightweight, portable and easy to carry. This has led to the rapid development of artificial intelligence applications on embedded platforms.
Embedded artificial intelligence platform