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This looks cool.

Currently the only real options for amateur off-the-shelf (accelerated) edge ML are the Nvidia boards (but small carrier boards for the TX2 cost more than the module itself) or the Intel NCS which inexplicably blocks every other USB port on the host device due to its poorly designed case. There is the Movidius chip itself, but Intel won't sell you one unless you're a volume customer. The NCS also does bizarre things: the setup script will clobber an existing installation of opencv with no warning, for example.

There are various optimised machine learning frameworks for ARM, but I'm only counting hardware accelerated boards here. I'm also not including the various kickstarter or indiegogo boards which might as well be vapour ware.

There are no good, cheap, embedded boards with USB3 that I can find. There are a few Chinese boards with USB3, but none of them have anywhere near the quality of support that the Pi has.

Then camera support. The Pi has a CSI port, but it's undocumented and only works with the Pi camera. The TX2 is pretty good, but you need to dig through the documentation to figure things out. USB is fine, but CSI is typically faster and frees up a valuable port.

Finally another issue is fast storage. It's difficult to capture raw video on the Pi because you can't store anything faster than about 20MB/s. There are almost no boards that support SATA or similar (the TX2 does), so the ability to use USB3 storage would be welcome too.

If this is offered at a reasonable price point, it could be a really nice tool for hobbyists. It looks like they're trying to keep GPIO pin compatibility with the Pi too.






> If this is offered at a reasonable price point

Hopefully it will, since the voice and vision kits on the same AIY page are sold for $50 at Target.

> There is the Movidius chip itself, but Intel won't sell you one unless you're a volume customer

Single units are listed on this page, e.g. mini-PCIe board with Movidius VPU for $79: https://up-shop.org/25-up-ai-edge


Good find, although they're not out yet by the looks of it? And also pushing the price of a TX2 if you want the dev board plus a vision carrier (though it does have 3 VPUs).

I was referring to the Movidius Dev Kit which exists, but seems impossible to buy as a consumer.


The Raspberry Pi provides documentation for their GPU architecture, so it would be possible to provide support for that within open source machine learning frameworks. It would involve quite a bit of work, though, and the RPi is not really competitive with modern hardware in performance-per-watt terms, even when using GPU compute.

I believe, Idein did that. At least they regularly post impressively (for the Pi) fast examples to /r/raspberry_pi like https://redd.it/a5o6ou. It seems the result isn't available individually or as open source but only in the form of a service (https://actcast.io/)

There are some well optimised libraries, for example a port of darknet that uses nnpack and some other Neon goodies. You can do about 1fps with tiny yolo. Not sure if it used anything on the gpu though.

NEON is the CPU SIMD feature, it has nothing to do with the vc4 GPU.

Yes, I know. My point was that CPU-only deep learning is possible on the Pi if you don't need real-time inference. What I wasn't sure of is whether that specific port does anything on the GPU at all, or if it's only using NEON intrinsics.

The Pi CSI connector can be used with any camera so long as you don't care for the ISP of the VideoCore GPU. If you want ISP then yeah, you need one of the devices the Foundation supports.

I think there's also a few boards out there based on the RK3399Pro (not sure how it compares performance-wise though).

The rock960 boards do look nice, and tick a lot of boxes in terms of peripherals. My only concern is documentation.

There's a review here though: https://fossbytes.com/rock960-review-affordable-six-core-arm...


That review is disappointing because he doesn't even run a benchmark to test if it's suitable for machine vision.

I just bought one, largely for machine vision. What benchmark would you be interested in?



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