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Another take from us at Edge Impulse at explaining TinyML / Edge ML in our docs: https://docs.edgeimpulse.com/docs/concepts/what-is-embedded-machine-learning-an
by furtiman 3y ago
Another take from us at Edge Impulse at explaining TinyML / Edge ML in our docs:
https://docs.edgeimpulse.com/docs/concepts/what-is-embedded-machine-learning-anyway https://docs.edgeimpulse.com/docs/concepts/what-is-embedded-...
We have built a platform to build ML models and deploy it to edge devices from cortex M3s to Nvidia Jetsons to your computer (we can even run in WASM!)
You can create an account and build a keyword spotting model from your phone and run in WASM directly
https://edgeimpulse.com https://edgeimpulse.com
Now another key thing that drives the Edge ML adoption is the arrival of the embedded accelerator ASICs / NPUs / e.g. that dramatically speed up computation with extremely low power - e.g. the Brainchip Akida neuromorphic co-processors [1]
Depending on the target device the runtime that Edge Impulse supports anything from conventional TFLite to NVIDIA TensorRT, Brainchip Akida, Renesas DRP-AI, MemryX, Texas Instruments TIDL (ONNX / TFLite), TensaiFlow, EON (Edge Impulse own runtime), etc.
[1] https://brainchip.com/neuromorphic-chip-maker-takes-aim-at-the-edge/ https://brainchip.com/neuromorphic-chip-maker-takes-aim-at-t...
[Edit]: added runtimes / accelerators
- moh_maya 3y agoI tried your platform for some experiments using an arduino and it was a breeze, and an absolute treat to work with. The platform documentation and support is excellent. Thank you for developing it and offering it, along with documentation, to enable folks like me (who are not coders, but understand some coding) to test and explore :)
- KingFelix 3y agoWhat sort of experiments did you do? I will go through some of the docs to test out on an arduino as well, would be cool to see what others have done!
- moh_maya 3y agoGesture recognition using the onboard gyroscope and accelerometer (I think - it was 2 years ago!), and it took me some part of an afternoon. I also used these two resources (the book was definitely useful; less sure if the arduino link the the same one I referred to then), which I found to be useful: [1] https://docs.arduino.cc/tutorials/nano-33-ble-sense/get-started-with-machine-learning/ https://docs.arduino.cc/tutorials/nano-33-ble-sense/get-star... [2] https://www.oreilly.com/library/view/tinyml/9781492052036/ https://www.oreilly.com/library/view/tinyml/9781492052036/
- furtiman 3y agoYou can check out the public project registry where community shares full projects they've built You can go ahead and clone any one you like to your account, as well as share a project of your own! https://edgeimpulse.com/projects/all https://edgeimpulse.com/projects/all
- furtiman 3y agoThis is amazing to hear! Good luck with any other project you're gonna build next! I can recommend checking out building for more different hardware targets - there is a lot of interesting chips that can take advantage of Edge ML and are awesome to work with