Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

TinyML: Ultra-low power machine learning

ikkaro.net

Post reply on HN
Print view View original

31–40 of 101 posts

Re: TinyML: Ultra-low power machine learning

#31

Post by moh_maya »

KingFelix wrote: post #30
moh_maya wrote: post #19

Earlier quoted context omitted.

I 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 :)

What 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!

Gesture 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-star...

[2] https://www.oreilly.com/library/view/tinyml/9781492052036/

Re: TinyML: Ultra-low power machine learning

#32

Post by cooootce »

I had the opportunity to work on TinyML, it's a wonderful field! You can do a lot even with very small hardware.

For example, it's possible to get real-time computer vision system with an esp32-s3 (dual-core XTensa LX7 @ 240 MHz cost like 2$), of course using the methods given in the article (Pruning, Quantization, Knowledge distillation, etc.). The more important thing is to craft the model to fit as much as possible your need.

More than that, it's not that hard to get into, with solution named AutoML that do a lot for you. Checkout tool like Edge impulse [0], NanoEdge AI Studio [1], eIQ® ML [2]

There is a lot of tooling that is more low-level too, like model compiler (TVM or glow) and Tensorflow Lite Micro [3].

It's very likely that TinyML will get a lot more of traction. A lot of hardware companies are starting to provide MCU with NPU to keep consumption as low as possible. Company like NXP with the MCX N94x, Alif semiconductor [4], etc.

At my work we have done an article with a lot of information, it's in French but you can check it out: https://rtone.fr/blog/ia-embarquee/

[0]: https://edgeimpulse.com/

[1]: https://stm32ai.st.com/nanoedge-ai/

[2]: https://www.nxp.com/design/design-center/software/eiq-ml-dev...

[3]: https://www.tensorflow.org/lite/microcontrollers

[4]: https://alifsemi.com/

Re: TinyML: Ultra-low power machine learning

#33

Post by janjongboom »

coolThingsFirst wrote: post #12

Uses of TinyML in industry: Uhm.... well... hehe

Things Edge Impulse customers have in production: Sleep stage prediction, fall detection for elderly, fire detection in power lines, voice command recognition on headsets, predicting heath exhaustion for first responders, pet feeders that recognize animals, activity trackers for pets, and many more.

Re: TinyML: Ultra-low power machine learning

#34

Post by hinkley »

winrid wrote: post #15

I imagine a future where viruses that target infrastructure could be LLM powered. Sneak a small device into a power plant's network and it collects audio, network traffic, etc and tries to break things. It would periodically reset and try again with a different "seed". It could be hidden in network equipment through social engineering during the sales process, for example, but this way no outbound traffic is needed -…

Conversely, the simpler the models on a system under attack, the more exploits start to resemble automated social engineering. I can easily develop my own model that understands the victim well enough that I can predict its responses and subvert them.

Re: TinyML: Ultra-low power machine learning

#35

Post by Qwertious »

modeless wrote: post #7
synergy20 wrote: post #5

TinyML is like IoT: great on concepts, everyone agrees it's the future, but has been slow to take off. or, maybe it's just that they're being built into all products now, they just do not need the brand for them such as IoT or TinyML.

I don't agree that TinyML is the future, just as I don't think IoT is the future. The future is robot servants. They will be ~human scale and have plenty of power to run regular big ML. In fact, I hope my home has fewer smart devices in the future. I don't need an electronic door lock if my robot butler unlocks the door when I get home. I don't need smart window shades if the butler opens and closes them whenever I w…

>I don't agree that TinyML is the future, just as I don't think IoT is the future. The future is robot servants. They will be ~human scale and have plenty of power to run regular big ML.

I swear I've read an article on exactly why human-scale robot servants make no sense.

It's something like:

1. Anything human-scale will tend to weigh as much as a human. That means it needs a lot of batteries, compared to e.g. a roomba. Lots more material and lots more weight means lots more cost. 2. Also, they'll be heavy. Which means if they e.g. fall down the stairs, they could easily kill someone. 3. If they run out of power unexpectedly (e.g. someone blocks their path to the charger) then they'll be a huge pain in the ass to move, because they're human scale. Even moreso if they're on the stairs for some reason.

Re: TinyML: Ultra-low power machine learning

#36

Post by hinkley »

moffkalast wrote: post #24
winrid wrote: post #15

I imagine a future where viruses that target infrastructure could be LLM powered. Sneak a small device into a power plant's network and it collects audio, network traffic, etc and tries to break things. It would periodically reset and try again with a different "seed". It could be hidden in network equipment through social engineering during the sales process, for example, but this way no outbound traffic is needed -…

Reminds me of this HN post a week back: https://news.ycombinator.com/item?id=38917175 Genuinely could be the same setup with a 8GB Pi 4 or 5, slap it into a network cabinet with power and ethernet and just let it rip. Maybe with an additional IMU and brightness sensor, then it can detect it's been picked up and discovered so it can commit sudoku before it's unplugged and analysed.

> can commit sudoku

Autocorrection is a giant pain in the ass.

Re: TinyML: Ultra-low power machine learning

#37

Post by moffkalast »

coolThingsFirst wrote: post #12

Uses of TinyML in industry: Uhm.... well... hehe

Turns out there's not much you can train when like 5 parameters fit into the entire memory of a microcontroller. Oh and you also need to read the sensors and run a networking stack and... yeah.

Re: TinyML: Ultra-low power machine learning

#38

Post by furtiman »

KingFelix wrote: post #30
moh_maya wrote: post #19

Earlier quoted context omitted.

I 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 :)

What 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!

You 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

Re: TinyML: Ultra-low power machine learning

#39

Post by furtiman »

moh_maya wrote: post #19
furtiman wrote: post #11

Another take from us at Edge Impulse at explaining TinyML / Edge ML in our docs: 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.c…

I 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 :)

This 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

Post reply on HN