Edge AI is still new and many people are not sure which hardware platforms to choose for their projects. Today, we will compare a few of leading and emerging platforms.
Nvidia has dominated AI chip with its GPUs since the boom of deep learning starting in 2012. Although they were power hungry, noisy and expensive (blame Bitcoin gold rush), there wasn’t other alternative and we had to tolerate with them. About 3 years ago, Google announced they have designed Tensor Processing Unit (TPU) to accelerate deep learning inference speed in datacenters. That triggered rush for established tech companies and startups to come out with specialised AI chip for both datacenters and edge.
What we will talk today is platform for edge AI. So, what exactly is edge AI? The term of edge AI is borrowed from edge computing which means that the computation is happening close to the data source. In AI world, now it generally means anything that is not happening in datacenter or your bulky computers. This includes IoT, mobile phones, drones, self-driving cars etc which as you can see, actually varies greatly in term of physical size and there are many vendors.
We will therefore focus our focus in platforms that are small enough to fit into pockets comfortably and that individual and small companies could purchase and use. Nvidia did a good job of its competitions in the following benchmark comparisons, and we have — Intel Neural Computer Stick, Google Edge TPU and its very own Jetson Nano.
