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Source:https://github.com/SoraKumo001/next-streaming

⬅️ BeagleY-AI: a 4 TOPS-capable $70 board from Beagleboard
mynegation 32 daysReload
I wish there was some reference of what can I actually do with 4 or 8 TOPS and 4 or 8 or whatever GiB of accelerator memory. Can I run speech recognition? Run a model for object detection in images? In video? LLMs - probably not. Stable Diffusion also seems out of question. But really - what can I run?
Alupis 32 daysReload
BeagleBoard is akin to a Raspberry Pi. It's not meant to be a powerhouse PC, or in this case, a powerhouse AI computing platform.

It's meant for embedding, tinkering, and learning.

To that end, 4GB of RAM on an AI Accelerator board is fine - the expected workloads will not consume a lot of RAM. This also makes the lack of NVMe sufficient as well.

For more "horsepower" there is also the BeagleBone AI-64[1], which claims up to 8 TOPS.

[1] https://www.beagleboard.org/boards/beaglebone-ai-64

rcarmo 32 daysReload
This looks interesting, but the trouble with these small built in accelerators is that they were mostly designed when YOLO was the pinnacle of edge applications. These days they’re grossly underpowered…
cjs_ac 32 daysReload
I find it interesting that the single-board computer market seems to be coalescing around the Raspberry Pi B models as the standard form factor. This device in particular has almost all of the same IO connectors as the Raspberry Pi 5 (one microHDMI port is missing) in all the same places, so it should be compatible with Raspberry Pi 5 cases. I wonder whether the pinout on the 40-pin header is the same as that on the Pi?

I think most of us here are familiar with the fact that amd64 machines are made entirely from commodity parts: ATX cases, ATX power supplies, and so on. I wonder whether there's a similar commodification in the offing for the Pi form factor?

kitd 32 daysReload
4 TOPS-capable

So, reach out & it'll be there?