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The BeagleBone AI is Available
- ycombonator 7y agoWhat are the typical use cases for this type of board ?
- m0zg 7y agoWhat's so "AI" about it? It doesn't even have a TPU. Kendryte K210 has a fixed point TPU, 400MHz dual core RISC V with FPU, 8 channel audio DSP, FFT and crypto acceleration, and costs $8.90 with wifi and $7.90 without. And the module is the size of a half of a postage stamp. Runs TensorFlow Lite (a subset of ops, but good enough to do practical things).
- mkl 7y agoThat has only 8MB RAM, and the company seems so fly-by-night they can't be bothered setting up Let's Encrypt.
- m0zg 7y ago8MB RAM is more than enough to run a quantized MobileNet, which they demonstrate by preloading object detection on it, out of the box. And the chip is real. I have a couple of boards with it, it works. I guess people just have a hard time believing all the stuff in the spec can be done for less than 10 bucks. 28nm by the way, not a joke. The company got its start in crypto mining, so this is a side gig for them.
- SerJaime 7y agoIt looks pretty cool and I think about getting one. Does anyone know if it has a dedicated neural network accelerator?
- oceanghost 7y ago>low cost development board yet, and it’s driving a whole new AI revolution. This press release is a disaster as far as grammar is concerned. I am legitimately unable to tell if it has any special properties regarding AI. And NO, it came out yesterday, it's not driving any revolutions.
- gapo 7y agoCan any of the better minds out here compare this with the Jetson/Jetson Nano ?
- kbumsik 7y agoIt's much more focused on real time controls (4 PRUs and 2 Cortex M4) than GPGPU processing. It's pretty much for industrial vision applications than the AI actually.
- kbumsik 7y agoDual Cortex-A15, 2 DSPs, 4 Vision Engines, 4 Real-time controllers (PRUs), 2 Cortex-M4s, 2D accelerators, dual 3D GPUs... It's impressive but, being pretty much domain specific chip, can anyone make use of its capabilities at hobbyist levels where Beaglebone is targeting?
- headcanon 7y agoDescription on the main page says: > Focused on everyday automation in industrial, commercial and home applications. So looks like they're trying to expand utility beyond hobbyist market.
- jrockway 7y agoI think if you're a true hobbyist, the Raspberry Pi is the path of least resistance. I've done projects with both the RPI and the Beaglebone Black. RPi is very aimed at taking some sort of existing project and modifying it, which sounds mean to say but actually lets you do some very powerful things. The average RPi user will never be compiling device tree overlays, screwing around with the bootloader, busting out the datasheet for the processor, etc. You find some project that is kind of like the one you want to do, use their hardware and low-level settings (involving device tree overlays, but so abstracted that you will never encounter that term), and then go to town on the software, probably written in something like Python. The Beaglebone is more like a dev board for TI's SoC. You will configure every pin exactly as you want it with a device tree overlay. You will be reading the datasheet to figure out exactly how many CPU cycles a certain instruction takes on the PRU. The amount of power you have over IO and hooking them into Linux is infinite. As an example, you can control the clock signal for the entire board through an IO pin. If you are doing realtime processing and want to make something happen in the Beaglebone PRU at the exact same time as some external hardware, you have the power to make that happen. Most people do not need this. The TLDR is that I would use the RPi for pretty much any "maker" project because it's so easy to get things working, but would use this new Beaglebone for something like a CNC machine. If you're making a CNC machine, you need a microcontroller to stop the motor instantly when it drives into the endstop (even if Linux is currently processing your mouse movement or a network packet), you need a realtime microcontroller to properly move the axes in unison (so you can cut a circle at your exact feedrate), and you need Linux to drive a monitor with the controls on it. This new board puts all the processing power you need on the same board, and has all the kernel hooks you need to communicate between your non-realtime Linux software and the microcontrollers. (It's been a couple years since I've used the PRUs, but they show up as a "remote CPU" under Linux and have APIs for bidirectional message passing, which is potentially more powerful than a serial port interface to an Arduino that does the realtime stuff.) You can of course just plug an Arduino into a USB port to get the same effect... this is how pretty much every 3D printer ever works. I literally have this exact setup on my 3D printer; an RPi that controls my 3D printer over USB -- the RPi hosts a web interface, the microcontroller handles the realtime motor moves. If you are manufacturing something like this, having everything on one board will probably lower your costs. That is why the Beaglebone exists. (What this has to do with AI? I don't know. Maybe it's for pick-and-place machines that need realtime motor moves and some computer vision.)
- trollian 7y agoDSP and vision does not "AI" make.
- monocasa 7y agoFWIW, the SGX GPU in it were on of the first to go hog wild on f16. And if they'd document their ISA, it's pretty amenable to being used for neural networks, wayore than the other mobile GPUs at least. It'd be a cold day in hell before they did that though unfortunately.
- UncleOxidant 7y agoTheir ISA isn't even documented?
- monocasa 7y agoNo. I actually looked at reverse engineering it a few years back. If you pull apart their drivers you can figure it out pretty quickly (there's actually two different ISAs, the main shader cores and a tiny little RISC esque core that marshals work for the shader cores). But their driver/software complexity is super high to even get a triangle in a buffer or run a compute job. They have a RTOS looking microkernel running on the shader cores, and there's a ton of caching and MMU setup you have to do from the GPU side (not the main app processor). And there's a lot of caching hints and hacks that are hard to work around if you don't know the context (a lot of tables for bug reference numbers and special cased code depending on those) If anyone from Imagination is listening, the open source community would still love your help in supporting these chips. : ) They're really pretty inside, and the world should know about the good work y'all did!
- missosoup 7y agoAny experiences with using tensorflow models with TI Deep Learning (TIDL)?
- UncleOxidant 7y agoDoes anybody actually use TIDL? Sounds more like a "me too" development from TI just so they can say they're in the DL game.
- jjoonathan 7y agoI'd be interested in hearing anecdotes as well. In my experience, software from hardware companies has been so reliably abysmal that it makes the "enterprise software" us SWEs like to complain about look decent by comparison. Hopefully it's different this time :)
- ausjke 7y agoHow does this comparing to Nvidia's Jetson-Nano(https://developer.nvidia.com/embedded/jetson-nano-developer-kit https://developer.nvidia.com/embedded/jetson-nano-developer-... for $99) which is cheaper and appears to be more powerful? I used BB in previous projects, one thing definitely stands out for BB is that, it could be used as a product directly with a case and some certification(EMC,etc). Nvidia's Nano is more of a development platform. Beagleboard predates RPi actually, though after Arduino, BB is arguably the very first board running a 32-bit ARM that is also open source, cheap, small, however it's overshadowed by RPi in recently years.
- castratikron 7y agoYeah the Beagleboard and more generally the TI AM335x is getting old. Single core, DDR3-800, no PCI-e, no secure boot, and other bizarre limitations. It's nice that they're putting something newer out.
- Quarrelsome 7y agoIs it just me or is the ® symbol being everywhere a bit off-putting (in terms of parsing the text)?
- rodiger 7y agoI feel like I'm reading an infomercial
- Stratoscope 7y agoIt's not just you. Any trademark expert will tell you that you only have to use the symbol once, in the first or most prominent place where you use the trademark. Also when you use other companies' trademarks, you should have a notice of who the trademark owner is, which this press release does for Sitara but not for the other trademarks used in the piece.
- msla 7y ago> Also when you use other companies' trademarks, you should have a notice of who the trademark owner is No, you shouldn't. There's no evidence it's required.
- msla 7y ago> Also when you use other companies' trademarks, you should have a notice of who the trademark owner is Do you have a cite for this? Thanks.
- Stratoscope 7y agoSure, here is one: http://www.bpmlegal.com/tmdodont.html http://www.bpmlegal.com/tmdodont.html Search the page for "disclaimer", or just read the whole article - it has a lot of good trademark advice. It also somewhat disagrees with what I said about only using the trademark symbol once - it says to use it the first time and "occasionally thereafter". So here is an interview with an IP attorney who suggests just using it once: https://www.forbes.com/sites/work-in-progress/2014/03/12/when-and-how-do-i-have-to-use-trademark-symbols/ https://www.forbes.com/sites/work-in-progress/2014/03/12/whe... (Disclaimer: I'm not a trademark expert! Just passing along what I've read from those who claim to be...)
- penagwin 7y agoIs it just me or does 1GB of ram seem a little low for a $100+ board? I can't seem to find what speed it is either. I'm not expecting anything crazy like 8gb or anything like that, but given how many boards sell at ~50$ with 4GB of ram this just seems kinda limited.
- TickleSteve 7y agoThe difference is the market size... you're comparing a niche board to a mass-market one. That is never going to be a fair comparison.
- rumanator 7y agoFor future reference, a Raspberry Pi 4 Model B+ with 4GB of RAM is being sold for around 60$.
- penagwin 7y agoThat's fair, I'm just a little confused because the BeagleBone AI board isn't even trying to compete with the <$100 boards. I know it's meant for very specific industrial uses, but depending on your models and how you have it setup, 1GB of ram isn't very much. If these are meant to be nodes in a larger system I guess that makes a little more sense, but if they're meant to be more autonomous that 1GB is going to be a real limitation for certain applications.
- Gibbon1 7y agoWhat I saw when I was looking (a few years ago) is the Beagle bone is oriented towards the industrial market/dev board for TI Industrial processors. Totally different than the hobbyist market.
- mumblemumble 7y agoI think that it probably doesn't make sense to compare this to a general-purpose single board computer. It's apples and oranges. The BeagleBone AI is aimed at prototyping in industrial automation applications. I've never worked in that area, but I wouldn't be at all surprised to see that large amounts of RAM isn't a priority for industrial controllers. Probably the software tends to be frugal with memory, because a bigger heap means more cache misses, and more cache misses mean worse latency. A Raspberry Pi, by contrast, is mostly targeted at running a GUI and memory-hungry user applications up to and including Minecraft. It's meant for teaching kids to program and hobby stuff. It doesn't have built-in DSPs and programmable real-time units, because those are for supporting applications that fall far outside its intended purpose of having fun with Python.
- kumarvvr 7y agoCan someone tell me if there are easy to use libraries that can speed up existing ML code, say written in Python, on this? Or do we have to write custom C/C++ code to make best use of available hardware?
- tiborsaas 7y agoThe press release could have used a real life example, like "Training MNIST dataset takes .5 seconds" or something. Where can I find info about how these edge computing boards are speeding up training time? Or how they compare to a 1080i?
- rcarmo 7y agoI'm not overly familiar with TI's SOCs post-2010. Anyone out there with a good overview of what the Sitara AM5729 includes besides the bullet points in that piece? And what about TIDL adoption? I've been working on the Intel/NVIDIA-grade part of the ML scale and have a few ESP32 boards to fiddle with OV2640 cameras, but very little in between except what Broadcom has been doing.
- Zenst 7y agoNot a summary (which would be from one perspective or another) the PDF's cover much and can wordsearch down into the details that pick your interest. http://www.ti.com/product/AM5729/technicaldocuments http://www.ti.com/product/AM5729/technicaldocuments As for tooling, could not say, but as a previous comment stated - the NVIDIA offering for the same money, makes this hard to stick out for many. Though i'm sure it, as most boards do, have a niche - what that niche is beyond already invested in the beagleboard environment and comfort level, that would be the only uptick for some that i'm seeing from initial glance.
- Abishek_Muthian 7y agoAlthough 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.
- neuromancer2701 7y agoBeagleBone is dual-core A15 vs Jetson Quad A53 so that is a big difference there is processing power as well.
- ris 7y agoIn 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.
- Abishek_Muthian 7y agoCorrection, 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.
- Isamu 7y agoThanks for pointing out the Nano, seems they are pricing it at $99: https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-nano/ https://www.nvidia.com/en-us/autonomous-machines/embedded-sy...
- 0x8BADF00D 7y agoThat’s pretty decent. I think Renesas has a devboard around the $125 price range. But it’s not as well specced.