edgetpu VS sb3_to_coral

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/

I did that using stable baselines 3, all my code is here: https://github.com/chunky/sb3_to_coral

The models were output from stable baselines 3. You can find the code I used to create those charts in my repo here: https://github.com/chunky/sb3_to_coral

I have code that does that, here; https://github.com/chunky/sb3_to_coral/blob/master/run_tflite.py

I had a personal endgame of deploying a trained agent to an embedded device, which turned out to be pretty easy once I was using SB3. FWIW, this is the code I wrote to convert trained SB3 agents through to TFLite and EdgeTPU: https://github.com/chunky/sb3_to_coral

But I'm unsure if this is actually the fastest way to get the data in obs, into the input tensor, on a coral. The slightly awkward reshape is an artifact of the conversion process I built from pytorch to TFLite.

I also had an easier time deploying a sb3 trained agent than I've had with anything else. https://github.com/chunky/sb3_to_coral