Over the last six months I’ve been looking at machine learning on the edge, publishing a series of articles trying to answer some of the questions that people have been asking about inferencing on embedded hardware.
But, after a half year of posts, talks, and videos, it’s all bit of a sprawling mess and the overall picture is of what’s really happening is rather confusing.
So here’s a great big benchmarking roundup!
Excellent overview… and …
The Raspberry Pi 4 is probably the cheapest, most affordable, most accessible way to get started with embedded machine learning right now. Use it on its own with TensorFlow Lite for competitive performance, or with the Coral USB Accelerator from Google for ‘best in class’ performance.
Stop breadboarding and soldering – start making immediately! Adafruit’s Circuit Playground is jam-packed with LEDs, sensors, buttons, alligator clip pads and more. Build projects with Circuit Playground in a few minutes with the drag-and-drop MakeCode programming site, learn computer science using the CS Discoveries class on code.org, jump into CircuitPython to learn Python and hardware together, TinyGO, or even use the Arduino IDE. Circuit Playground Express is the newest and best Circuit Playground board, with support for CircuitPython, MakeCode, and Arduino. It has a powerful processor, 10 NeoPixels, mini speaker, InfraRed receive and transmit, two buttons, a switch, 14 alligator clip pads, and lots of sensors: capacitive touch, IR proximity, temperature, light, motion and sound. A whole wide world of electronics and coding is waiting for you, and it fits in the palm of your hand.