WickeyTips

With this series now we are moving some another path of Embedded Systems. So what you can think of deep learning, machine learning, and artificial intelligence. I'm sure you have followed some courses, books, articles regarding this subject. Me too followed these courses to learn this subject. We have followed a lot of mathematics modules and stuff with this subject. So, I would like to share my experience of applying deep learning frameworks to our daily embedded systems applications. That's why I started this tutorial series. Hardware? I thought about running a neural network to detect plant behavior using raspberry pi once :-).I tried it with my raspberry pi and totally lagged. Raspberry PI has an ARM processor consist of few cores optimized for sequential serial processing. This will not enough for real-time processing. A CPU consists of a few cores optimized for sequential serial processing - See more at: http://www.nvidia.com/object/what-is-gpu-computin...

This is credit card size small form-factor Linux system-on-module, destined for demanding embedded applications in visual computing. We are using this board for the applications of our embedded systems because it offers enormous GPU processing in a tiny computer that only consumes 5-20 watts of power. Aside from the GPU, the CPU is certainly not slow with four 64-bit A57 ARM cores. And, you have 4GB of RAM and 16GB of eMMC storage, so you should be able to load your application onto the onboard storage and throw around big chunks of data in RAM. The SATA interface gave a great performance when paired to an SSD, and the two antenna 802.11ac gave speeds up to Gigabit Ethernet over the air. Specifications