edgetpu VS frigate

I know the tensor power pixelbook was shutdown and I never heard the actual reason just a bunch of speculation about costs/profitability which is probably true.

It's a shame that there isn't more competition and development in the neural asic world to harness the power of llms/generative AI on a low power, cheap hardware platform like the pixelbook line. For someone that invented the TPU they have done a not so great job of ensuring it's commercialization and support. Both on the hardware and software side.

The coral edge tpu seemed to be the right high level idea but without proper execution.

https://github.com/google-coral/edgetpu/issues/668

> even in the 80s I wanted an FPGA accelerators in every machine

Mostly unrelated, but I recently discovered that you can buy TPUs, right now, as a consumer product, from https://coral.ai.

The stock firmware already allows you to run these things so hard they overheat, which is amazing.

But yes, I also want FPGA accelerators.

I'm looking at the coral.ai cards and they have a M.2 A+E card, same form factor as the wifi slot in the m900 tiny. Has anyone tried another card in that slot other than wifi?

Chips optimized to perform the type of calculations used for NN inference at high parallelism. A good example would be the google spinoff https://coral.ai/ (though their usecase is highly limited by sub-par software constraints)

By no means an expert, but I have seen prototypes using a raspberry pi and a dongle from Coral Ai. They have PCIE and USB based modules.

Last news on https://coral.ai/ was on May 5 2022

Activity on the github project seems to have stopped. https://github.com/google-coral

    https://coral.ai/

Your problem statement still needs more clarification. If the above applies, the best way is to evaluate your ML-based pattern matcher on high-level scenarios. One approach to speed up the evaluation is to lift and shift the execution of scenarios into cloud. Another approach is to use an AI accelerator, such as http://coral.ai or other.

You probably could but not with raspis. Maybe the TPUs they sell. https://coral.ai/

Am in to selfhosting and homeserver, finally got to try Frigate with some aliexpress camera that was not mine. Love it.

However, I have had success using IP Camera app with Frigate. https://play.google.com/store/apps/details?id=com.pas.webcam https://frigate.video/

Frigate https://frigate.video/ and ZoneMinder https://zoneminder.com/ come to mind. Blue Iris https://blueirissoftware.com/ is not open source but is what I prefer to use for my PoE systems ($80/yr)

Logger: custom_components.frigate Source: custom_components/frigate/__init__.py:201 Integration: Frigate (documentation, issues) First occurred: 1:59:34 AM (2 occurrences) Last logged: 1:59:48 AM

Engineering implies working within constraints. Most people in this realm only have to deal with the spouse acceptance factor as a limiter.

Went from openhab -> homeassistant -> Node-RED. Then sprinkle in MySensors, Frigate, and Double-Take, but not on just the entrance, go for the perimeter then defense in depth.

https://www.mysensors.org/

https://frigate.video/

https://github.com/jakowenko/double-take

- https://github.com/blakeblackshear/frigate/discussions/7932#...

I have found my frigate+ model to be much more accurate and crazy good even at night. Will be curious how things change when it snows here more often, since I've not submitted any examples of winter at this house yet.

According to the author of that PR, they're using 10% of 1 NPU core on 3 cameras: https://github.com/blakeblackshear/frigate/pull/8382#issueco...

The bottleneck instead will probably be the video stream decoding speed, especially as the SoC's hardware decoder isn't being used yet.

2023-11-13 11:16:39.073 ERROR (MainThread) [custom_components.frigate] Using a Frigate server (http://ccab4aaf-frigate-fa:5000) with version 0.12.1-367d724 <= 0.12.1 which is not compatible -- you must upgrade: https://github.com/blakeblackshear/frigate/releases