4 ms·

This is a dev board, not a consumer product. And (contrary to the title) the press release explicitly says that it is not intended for "deep learning." Acceler

by tacos 10y ago

This is a dev board, not a consumer product. And (contrary to the title) the press release explicitly says that it is not intended for "deep learning." Acceleration is needed for training -- not running the models themselves. A quick glimpse of the power used (1 watt) lets you know exactly how much "acceleration" is going on in here. This is meant for tiny devices. EDIT: My point is that this is a small-run dev board for a chip for some future $19 nannycam. It's not an "accelerator" you install on your PC to put your graphics card to shame running TensorFlow. EDIT #2: This is another one of those HN threads that's overrun by enthusiasts. Jamming a chip onto a stick is simply how they sell embedded crap now. Here's a crypto chip that'll really get you guys going: http://www.atmel.com/tools/AT88CK590.aspx http://www.atmel.com/tools/AT88CK590.aspx

sailfast 10y ago
Sure, maybe a nannycam. Maybe whatever you want to run on your Arduino / Raspberry Pi. Maybe bolted on to a small lightweight drone swarm that communicates to itself while offline to learn about its environment. What happens when 1000 of these are plugged in to 1000 drones and they all communicate? I don't know enough to even guess but perhaps something interesting and dangerous?
- joaorj 10y ago
  At first glance I think they would take off and be airborne. Other than that they would do whatever they were programmed to do. I know, my comment is stupid, thought I should fit in.
petra 10y ago
>> Acceleration is needed for training -- not running the models themselves. Sometimes training is done on the customer site. For example a noise cancellation algorithm may learn audio characteristics of the user's environment to offer better performance.
dgacmu 10y ago
Acceleration is actually useful for both. If you're running in a constrained, mobile or sensor environment, then you really do want acceleration that improves your power efficiency. It's also useful if you do high volume serving, but that's obviously not the case here. (For example, Google's voice recognition on Android can run when offline.)
rm999 10y ago
>Acceleration is needed for training -- not running the models themselves This isn't true. Running neural networks (including CNNs) can be computationally and power intensive, and lends itself to the vector operations of GPUs, FPGAs, and ASICs. Putting the computations on devoted hardware could enable embedded applications that simply aren't possible otherwise. Here's a whitepaper by Microsoft about using FPGA's to speed up CNNs: http://research.microsoft.com/apps/pubs/?id=240715 http://research.microsoft.com/apps/pubs/?id=240715 Article by Google explaining the importance of optimizing neural networks to run on mobile phones: http://googleresearch.blogspot.com/2015/07/how-google-translate-squeezes-deep.html http://googleresearch.blogspot.com/2015/07/how-google-transl...
- zxv 10y ago
  Are there any good examples of FPGA implementations of CNN? I see one example in Verilog on github: https://github.com/ziyan/altera-de2-ann/blob/master/src/ann/ann.vhd https://github.com/ziyan/altera-de2-ann/blob/master/src/ann/...
  - dharma1 10y ago
    RNN - http://arxiv.org/abs/1511.05552v4 http://arxiv.org/abs/1511.05552v4
- tacos 10y ago
  The Google article specifically says they were able to solve the problem on the (tiny) CPU in the phone by using vector operations on the CPU. Again, this is a tiny doodad for power-starved devices that lack any micro. Have you played with Apple Accelerate? They've been baking this stuff into their chips for quite some time. Apple's FFT outperforms FFTW. https://developer.apple.com/library/mac/navigation/#section=Frameworks&topic=Accelerate https://developer.apple.com/library/mac/navigation/#section=...

by tacos 10y ago

sailfast 10y ago

Sure, maybe a nannycam. Maybe whatever you want to run on your Arduino / Raspberry Pi. Maybe bolted on to a small lightweight drone swarm that communicates to itself while offline to learn about its environment. What happens when 1000 of these are plugged in to 1000 drones and they all communicate? I don't know enough to even guess but perhaps something interesting and dangerous?

joaorj 10y ago
At first glance I think they would take off and be airborne. Other than that they would do whatever they were programmed to do. I know, my comment is stupid, thought I should fit in.

petra 10y ago

>> Acceleration is needed for training -- not running the models themselves. Sometimes training is done on the customer site. For example a noise cancellation algorithm may learn audio characteristics of the user's environment to offer better performance.

dgacmu 10y ago

Acceleration is actually useful for both. If you're running in a constrained, mobile or sensor environment, then you really do want acceleration that improves your power efficiency. It's also useful if you do high volume serving, but that's obviously not the case here. (For example, Google's voice recognition on Android can run when offline.)

rm999 10y ago

>Acceleration is needed for training -- not running the models themselves This isn't true. Running neural networks (including CNNs) can be computationally and power intensive, and lends itself to the vector operations of GPUs, FPGAs, and ASICs. Putting the computations on devoted hardware could enable embedded applications that simply aren't possible otherwise. Here's a whitepaper by Microsoft about using FPGA's to speed up CNNs: http://research.microsoft.com/apps/pubs/?id=240715 http://research.microsoft.com/apps/pubs/?id=240715 Article by Google explaining the importance of optimizing neural networks to run on mobile phones: http://googleresearch.blogspot.com/2015/07/how-google-translate-squeezes-deep.html http://googleresearch.blogspot.com/2015/07/how-google-transl...

zxv 10y ago
Are there any good examples of FPGA implementations of CNN? I see one example in Verilog on github: https://github.com/ziyan/altera-de2-ann/blob/master/src/ann/ann.vhd https://github.com/ziyan/altera-de2-ann/blob/master/src/ann/...
- dharma1 10y ago
  RNN - http://arxiv.org/abs/1511.05552v4 http://arxiv.org/abs/1511.05552v4
tacos 10y ago
The Google article specifically says they were able to solve the problem on the (tiny) CPU in the phone by using vector operations on the CPU. Again, this is a tiny doodad for power-starved devices that lack any micro. Have you played with Apple Accelerate? They've been baking this stuff into their chips for quite some time. Apple's FFT outperforms FFTW. https://developer.apple.com/library/mac/navigation/#section=Frameworks&topic=Accelerate https://developer.apple.com/library/mac/navigation/#section=...