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Comment by Abishek_Muthian | Hacker News Reader

Although it is priced competitively, ~ same as Nvidia Jetson Nano ($125) it seems underpowered when compared to Nano. Nano has 4GB RAM, 128 CUDA cores and can 4K encode/decode 30/60 FPS and also handle multiple streams when compared to 15/15? of BeagleBone.

Perhaps the Vision Engine is better for computer vision tasks, but having to use TIDL suite when compared to Jetson Nano's JetPack with tools which we use regularly on bigger GPUs is going be a hard compromise to make.

Jetpack includes CUDA 10, TensorRT, OpenCV 3.3.1 etc. by default and PyTorch is available separately for Jetson Nano. Besides the community is very active.

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Thanks for pointing out the Nano, seems they are pricing it at $99:

https://www.nvidia.com/en-us/autonomous-machines/embedded-sy...

That’s pretty decent. I think Renesas has a devboard around the $125 price range. But it’s not as well specced.

I bought Nvidia Jetson Nano Dev Kit in India as soon as it became available for (~$125), in USA it's bit cheaper at ~$110 (incl. shipping).

Please note that there is also Nano Module, which is a SOM (System on Module) with 16GB EMMC storage sold for $150. I think this is intended for clusters.

Something worth being aware of: the Jetson Nano development kit that's currently available has an early version of the Nano module which isn't pin-for-pin (finger-for-finger?) compatible with the current production version sold separately.

The production Nano module (which you can also now buy) won't work in the dev kit carrier! Similarly, the dev kit Nano module won't work in a carrier compatible with the production Nano module.

Apparently they'll refresh the development kit to match the production Nano module later in the year, but for now it's a big gotcha if you're designing hardware using the dev kit.

For some tasks (real-time) the BeagleBone's PRU's are a big advantage.

I guess it has its uses cases where just RTOS doesn't cut it. It would have been a lot easier if they had put up a detailed comparison.

BeagleBone is dual-core A15 vs Jetson Quad A53 so that is a big difference there is processing power as well.

In which direction? Remember, the A15 was designed as a "big" core while the A53 was considered a "LITTLE", having a design lineage closer to the A7.

Correction, Jetson Nano has Quad-core ARM A57@1.43 GHZ. They are the Big cores of most big/LITTLE configuration with better L1/L2 cache(48KB/2MB unified in case of Nano) than A53 cores.

Does anybody actually use these TI deep learning libs? I've never even heard of them before.

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