Distibuted compute

[stefanh12]

The Feature

I've been using AI-on-the-edge-device for a year now and its really great! It would be great if we could offload the processing of the image since it takes quite a long time to either multiple esp32 devices or to a docker container on HA or such. multiple esp32 devices could then split the digital / analog up and really speed up the handling. On HA as a container it would I guess only take seconds to process too.

[caco3]

But what will you save with this?

My devices have an interval of 2 minutes which is already far more what effectively is needed. Also it will not save you any power as the ESP32-CAM does not support power saving.

A goal of this project is to make usage as easy as possible for non-techy people. Having to learn about docker is way beyond most users will want to look into.

BTW, @jomjol originally had offline processing in docker but moved it all to the ESP.

[stefanh12]

For me running at 240mhz each processing takes 3 minutes with analysis of 4 digits and 4 analog clocks. I would like to speed this up since 3-4 minutes delay equals to around 70-90 liters of hot water when both kids shower. If we could distribute compute between 2-4 esp devices it would be 1,5 to less than a minute, resulting in better resolution and the possibility to take action before hot water is all spent.

Power management on an esp not running on battery is not really an issue

[Slider0007]

@stefanh12: If you'd like to speed up the process it is possible to optimize this without adding addtional computing power / complexity.

The following things affect the processing time.

• Take image / flash time (default: 5s) -> could be reduced to save some seconds
• Alignment time -> EITHER: It it possible to switch off the alignment algo and use just preset angle correction (alignment algo: off) -> OR: Reduce expert parameter 'search field X /Y' -> time reduction is quadratic to field area -> OR: The smaller the alignment marker the quicker the processing
• Image evaluation time: Use modern quantized tflite models (e.g. dig-class100_...q.tflite, ana-class100..._q.tflite)

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Just for reference:

• I have 3 digits and 2 analogs + activated alignment (160Mhz) -> Processing time: 16s

[friedpa]

@Slider0007: Would be in my opinion good to add this in the wiki. So if someone has needs to speed up, he can find this parameter setting szenario quickly.

Edit: I have not done any of the recommendations above and my Round Completed time is in average 35 sec

[stefanh12]

That helped a lot, processing time is now way lower!

[friedpa]

Do improve it further you can set the CPU frequency to 240MHz

[caco3]

Would be in my opinion good to add this in the wiki.

We are always open for support. I am also willing to show how to extend the documentation!

I have a device which processes 3 digits and 4 pointers, taking 50s at 160 MHz

[friedpa]

Ok, let me know how it works.

[caco3]

can you have a look on https://github.com/jomjol/AI-on-the-edge-device-docs/blob/main/README.md for an introduction? Afterwards we could do an online meeting to clarify open questions.

[friedpa]

Ok, mache ich...