Combining Inference, Real-Time Robotics, Machine Learning, and AI at the Embedded Edge with PHYTEC and TI Sitara™ Platforms

Creating the demo

Here at PHYTEC, we felt there is a need for a demonstration of all of these technologies interacting together on one efficient system. Our idea was to use one advanced application processor, capable of accelerating all the specialized tasks required by these new technologies, to control every aspect of the system. Texas Instrument’s Sitara® AM57x application processor provides a robust dual-core arm Cortex®-A15, discrete C66 DSPs, discrete PowerVR SGX54x GPUs, TI’s Embedded Vision Engines (neural network accelerators), dedicated image processors, and dedicated real-time communication processors. PHYTEC’s phyCORE-AM57x system on module utilizes the Sitara™ AM57x processor and provides an easy solution for development of our demo. So, with hardware in mind, we set off to figure out what we should build.

Now, the team here at PHYTEC didn’t want to make just any demonstration! We wanted to make it fun and interactive. Somehow, at the office, the idea of a robot playing Connect-Four® came up… To our surprise there happens to be a version of a Connect-Four® game using balls that you try to throw into the columns instead of pucks. The balls, compared to pucks,  would make it much easier for a robot to handle. However it came to be, it was decided that we will make a human vs machine version of Connect-Four®.

So after a few months (we had started this prior to CES) of serious 3D modeling and printing, software design, and embedded system design, we ended up with our Connect Four® demo, code named Connect-0100!

I made a short film that tries to explain the various components, how each part is being accelerated (some of the acceleration isn’t implemented yet, but we will get there), and an overall flow of the demo.