This is going to be a brief article. As you may already know, I'm working on a project to evaluate the feasibility of using AI algorithms on embedded platforms (apparently that's called The Edge now ¯\_(ツ)_/¯). We (me and Red Boumghar) are mainly interested in space applications, but the outcomes apply to many other fields.

If I had to highlight the key features of edge computing those would be low latency, security and privacy. Nevertheless I don't want to talk about this today. There are already many companies working on this and they have good resources to help us understand these advantages. You can read for instance the following blog posts from Qualcomm, Google, ARM and of course NVIDIA.

This field seems to be slowly but steadily growing. One year ago I could almost count the solutions available on the fingers of one hand, but this task is becoming increasingly difficult. To make it possible, I've decided to create a crowdsourced list on Github. I can't do it by myself, but I know I'm not alone and there're people with the same issue willing to help.

And that's all I wanted to say; if you are interested on edge computing, in particular on edge AI, check out the list and let's keep ourselves up-to-date.

Check out the crowdsourced list of resources for embedded AI at

Cover picture by Glenn Carstens-Peters.