Ask HN: What are your favourite hardware Dev Kits for AI development? | Hacker News Reader

Ask HN: What are your favourite hardware Dev Kits for AI development?

11 pointssurume10 comments

Combining AI with hardware features like video cameras and lidar is all the rage right now, and it would be very fun to hack something together.

What is your favourite hardware dev kit? (e.g. Nvidia Jetson Nano was all the rage a few years ago).

Main factors for consideration are processing power - it should be able to be used at a somewhat professional level, as well as ease of use - easy to moderate level of complexity are ideal.

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My absolute favorite has to be the Raspberry Pi 4 Model B combined with the Google Coral USB Accelerator. The processing power and versatility of the Raspberry Pi make it an ideal platform for many AI projects, and when you add the Coral USB Accelerator, you get the power of Google's Edge TPU, enabling efficient execution of machine learning models at the edge.

This combination offers a nice balance of processing power and ease of use, making it suitable for both hobbyists and professionals looking to develop and test AI solutions without investing in heavy server setups. Moreover, the community support for both Raspberry Pi and Coral is excellent, which makes troubleshooting and experimenting with new ideas easier.

Best of all, this setup won’t break the bank, making it a great way to get started with AI development without significant upfront investments.

Related to the Coral accelerator, is it usable as a general accelerator outside AI workflows? When I looked into it, there was no general SDK on using the Coral, only an "ingest ML model" compiler.

Out of curiosity, was this comment generated by an LLM (at least partially)?

The wording gives that kind of feel and now I'm wondering if my feeling around it is at all accurate.

Definitely. I don't understand why people feel they need to do that, especially here.

I wanted to address the comment you made. It's important to understand that large language models have become integral tools for skilled prompt engineers. This isn't an oddity but rather a natural evolution for this new professional field. In my view, your response seemed to carry a tone of prejudice and skepticism, which I find questionable. The advancement and integration of these models into various workflows are indicative of progress and innovation, not a cause for derision or doubt. I hope we can maintain a more open and respectful dialogue moving forward.

> Out of curiosity, was this comment generated by an LLM (at least partially)?

Same author as https://news.ycombinator.com/item?id=39876247

I've also used the Raspberry Pi + Coral TPU. For a while it was almost impossible to get the USB as it was always out of stock, but those issues seem to be gone. I would not, however, recommend the devboard as the documentation is quite sparse (runs on an NXP processor). RPi+Coral is a good combo, and the Pi5 supports the PCIe accelerators.

The BeagleBoard BeagleY-AI that was just released costs $70 at 4TOPS, equal to the Coral which is $60 for the USB alone disregarding the cost of a Pi [2]. It just came out, but BeagleBoards have quite a good community reputation and because the hardware is more open, easier to develop into a commercial product. The older BeagleBone AI-64 has a processor that supports 8TOPs, and TI has processors that go up to 32TOPs if you have the skills to create your own board (I don't).

I would not recommend developing with the NVIDIA Jetson unless you have particularly deep pockets and like to use an out-of-date toolchain. The latter isn't too unfamiliar to the embedded world though.

[1] https://pineberrypi.com/products/hat-ai-for-raspberry-pi-5 [2] https://www.beagleboard.org/boards/beagley-ai

Thank you so much for sharing your detailed insights and experiences! I'm particularly intrigued by the Raspberry Pi + Coral TPU combo, and it's reassuring to hear that the availability issues seem to have been resolved. The mention of the newly released BeagleBoard BeagleY-AI also caught my attention, especially given its cost-effectiveness and open hardware approach. I've appreciated the support from the BeagleBoard community in past projects, so the BeagleY-AI seems like an attractive option to explore.

Regarding the NVIDIA Jetson, your comments affirm some of my reservations about the costs and the currency of the toolchain. It seems like it might not be the best fit for my needs at this time.

Do you have any experience or tips on getting started with either the Raspberry Pi + Coral setup or the BeagleY-AI, particularly? Any advice on kickstarting a project and navigating potential pitfalls would be invaluable. Also, if you have any recommendations for resources or communities focused on these platforms, I would be extremely grateful.

Thanks for the suggestion. I just ordered a Coral USB to hopefully play around with.

Why not go with the Raspberry Pi 5?

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