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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&#x0027;t beat anyone at Go, but it opens the door to applications we might never even imagine otherwise. &#x2022; Specifically, I want to bring machine learning to the Arduino ecosystem. This has become recently possible thanks to improvements in hardware and software. &#x2022; On the hardware side, Moore&#x0027;s Law may be running out of steam when it comes to cutting-edge processors, but the party&#x0027;s not over when it comes to microcontrollers. Microcontrollers based on 8-bit automatic-voice-recognition processors dominated the Arduino ecosystem&#x0027;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. &#x2022; On the software side, a big step has been the release of Google&#x0027;s TensorFlow Lite, a framework for running pretrained neural networks &#x2014; also known as models &#x2014; on so-called edge devices. Last April, IEEE Spectrum&#x0027;s Hands On column looked at Google&#x0027;s Coral Dev Board, a single-board computer that&#x0027;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.

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