Making machine learning arduino compatible: A gaming handheld that runs neural networks - [Resources_Hands On]

I want to find out what happens when we bring machine learning to cheap, robust devices that can have all kinds of sensors and work in all kinds of environments. And I want you to help. The kind of AI we can squeeze into a US $30 or $40 system won't beat anyone at Go, but it opens the door to applications we might never even imagine otherwise. • Specifically, I want to bring machine learning to the Arduino ecosystem. This has become recently possible thanks to improvements in hardware and software. • On the hardware side, Moore's Law may be running out of steam when it comes to cutting-edge processors, but the party's not over when it comes to microcontrollers. Microcontrollers based on 8-bit automatic-voice-recognition processors dominated the Arduino ecosystem's early years, for example, but in more recent years, embedded-chip makers have moved toward more powerful ARM-based chips. We can now put enough processing power into these cheap, robust devices to rival desktop PCs of the mid 1990s. • On the software side, a big step has been the release of Google's TensorFlow Lite, a framework for running pretrained neural networks — also known as models — on so-called edge devices. Last April, IEEE Spectrum's Hands On column looked at Google's Coral Dev Board, a single-board computer that's based on the Raspberry Pi form factor, designed to run TensorFlow Lite models. The Coral incorporates a dedicated tensor processing unit and is powerful enough to process a live video feed and recognize hundreds of objects. Unfortunately for my plan, it costs $150 and requires a hefty power supply, and its bulky heat sink and fan limit how it can be packaged.