4 ms·

Where do FPGAs sit for running neural networks? Or, does it make sense to manufacture Field Programmable Node Arrays rather than Field Porgrammable Gate Arrays?

by kps 11y ago

akiselev 11y ago
I think that GPUs are already basically field programmable node arrays. Maybe for certain instances FPGAs would be better but the neural network would have to be designed at the logic gate level so it'd be significantly more costly in engineering time than a algorithm running on the GPU or CPU. FPGAs are for relatively simple logic running at incredibly high throughput with very low latencies. It's extremely common to move more complicated logic to a "soft" CPU on the FPGA that runs C code compiled to ARM/SPARC/POWER etc. and controls the low level hardware.
- striking 11y ago
  GPUs are getting closer to being math- and draw-optimized FPGAs. I don't think they'll be separate products too far in the future.
  - CamperBob2 11y ago
    At the desktop/server level, I agree, which is why I don't get the whole Intel/Altera synergy thing. But in more specific embedded cases -- meaning the vast majority of FPGA applications -- they are nothing alike. If I need to run a multichannel digital downconverter on a power budget of 1 watt, no GPU available in the foreseeable future is going to handle that role as well as an FPGA will. Perhaps someday a hypothetical I/O-optimized GPU will be able to do the jobs that are currently done on FPGAs and ASICs, but definitely not now.
    - PhantomGremlin 11y ago
      I don't get the whole Intel/Altera synergy thing Let's recall a previous acquisition, one that I totally didn't understand. An analyst had a really good quip about it: JPMorgan analyst Christopher Danely upgraded Intel to overweight following the company's earnings, although he still struggles to reconcile Intel's recent acquisition spree. "Intel might as well have bought Whole Foods," he said of the McAfee deal.[1] Compared to McAfee, acquiring Altera was brilliant. As a public company Intel is always facing scrutiny from Wall Street. They need to constantly increase both revenue and earnings. What better way than via a strategic acquisition? Instant growth. And Altera was a logical target. Altera sells high-margin silicon. Intel sells high-margin silicon. Intel was even fabbing some of Altera's parts, so they were quite familiar with the company. And maybe some smart people could eventually think of some synergy. E.g. once upon a time Intel made the IXP1200, a "network processor". What if, instead of designing custom silicon for something like that, perhaps a CPU + FPGA on a single die would be sufficient? Makes much more sense than Intel acquiring McAfee. [1] http://www.reuters.com/article/us-dealtalk-intel-idUSTRE73K7ZZ20110421 http://www.reuters.com/article/us-dealtalk-intel-idUSTRE73K7...
  - _yosefk 11y ago
    GPUs and FPGAs are very far from each other architecturally and are good at very different things. They will certainly be separate products for the next 3 decades as they were for the last 3 decades.
  - makomk 11y ago
    I think they'll continue to be seperate products far into the future because they're fundamentally different approaches. Aside from anything else, FPGAs are optimized for latency whereas GPUs are optimized for overall throughput.
bogomipz 11y ago
Can someone explain the "intersection" of neural networks and FPGAs/GPUs. I feel like I'm seeing this referenced a fair amount but haven't heard a good explanation on why this hardware is attractive for these workload.
- danielvf 11y ago
  Because neural networks use "simple" math that is massively parallelizable. Your computer's CPU may have eight cores, but my older, mid range graphics card has over a thousand cores. If your work can be broken down into a thousand peices that can be computed independently, than using a GPU can get you ludicrous speedups.
  - lucaspiller 11y ago
    This is a bit of an over simplification, because the 'cores' of a GPU aren't really comparable to a CPU core. It's more like scaling up a factory - at a single point on the production like you go from 1 person doing a job to 100 people doing the same job with the same tools. It means you can do more of the same thing, but can't do 100 completely different things. Here is a good overview: http://gamedev.stackexchange.com/a/17255 http://gamedev.stackexchange.com/a/17255
  - bogomipz 11y ago
    Thanks
- deleted 11y ago
  [deleted]
- SixSigma 11y ago
  I think it is something people just like to say because both sound a big magical. Neural nets are matrix multiplications and addition. They are generally memory constrained on GPUs and Main <> GPU memory is also a bottleneck. I don't see how an FPGA will really help there.
  - mtgx 11y ago
deleted 11y ago
[deleted]

Where do FPGAs sit for running neural networks? Or, does it make sense to manufacture Field Programmable Node Arrays rather than Field Porgrammable Gate Arrays?

by kps 11y ago

Where do FPGAs sit for running neural networks? Or, does it make sense to manufacture Field Programmable Node Arrays rather than Field Porgrammable Gate Arrays?

akiselev 11y ago

I think that GPUs are already basically field programmable node arrays. Maybe for certain instances FPGAs would be better but the neural network would have to be designed at the logic gate level so it'd be significantly more costly in engineering time than a algorithm running on the GPU or CPU. FPGAs are for relatively simple logic running at incredibly high throughput with very low latencies. It's extremely common to move more complicated logic to a "soft" CPU on the FPGA that runs C code compiled to ARM/SPARC/POWER etc. and controls the low level hardware.

striking 11y ago
GPUs are getting closer to being math- and draw-optimized FPGAs. I don't think they'll be separate products too far in the future.
- CamperBob2 11y ago
  At the desktop/server level, I agree, which is why I don't get the whole Intel/Altera synergy thing. But in more specific embedded cases -- meaning the vast majority of FPGA applications -- they are nothing alike. If I need to run a multichannel digital downconverter on a power budget of 1 watt, no GPU available in the foreseeable future is going to handle that role as well as an FPGA will. Perhaps someday a hypothetical I/O-optimized GPU will be able to do the jobs that are currently done on FPGAs and ASICs, but definitely not now.
  - PhantomGremlin 11y ago
    I don't get the whole Intel/Altera synergy thing Let's recall a previous acquisition, one that I totally didn't understand. An analyst had a really good quip about it: JPMorgan analyst Christopher Danely upgraded Intel to overweight following the company's earnings, although he still struggles to reconcile Intel's recent acquisition spree. "Intel might as well have bought Whole Foods," he said of the McAfee deal.[1] Compared to McAfee, acquiring Altera was brilliant. As a public company Intel is always facing scrutiny from Wall Street. They need to constantly increase both revenue and earnings. What better way than via a strategic acquisition? Instant growth. And Altera was a logical target. Altera sells high-margin silicon. Intel sells high-margin silicon. Intel was even fabbing some of Altera's parts, so they were quite familiar with the company. And maybe some smart people could eventually think of some synergy. E.g. once upon a time Intel made the IXP1200, a "network processor". What if, instead of designing custom silicon for something like that, perhaps a CPU + FPGA on a single die would be sufficient? Makes much more sense than Intel acquiring McAfee. [1] http://www.reuters.com/article/us-dealtalk-intel-idUSTRE73K7ZZ20110421 http://www.reuters.com/article/us-dealtalk-intel-idUSTRE73K7...
- _yosefk 11y ago
  GPUs and FPGAs are very far from each other architecturally and are good at very different things. They will certainly be separate products for the next 3 decades as they were for the last 3 decades.
- makomk 11y ago
  I think they'll continue to be seperate products far into the future because they're fundamentally different approaches. Aside from anything else, FPGAs are optimized for latency whereas GPUs are optimized for overall throughput.

bogomipz 11y ago

Can someone explain the "intersection" of neural networks and FPGAs/GPUs. I feel like I'm seeing this referenced a fair amount but haven't heard a good explanation on why this hardware is attractive for these workload.

danielvf 11y ago
Because neural networks use "simple" math that is massively parallelizable. Your computer's CPU may have eight cores, but my older, mid range graphics card has over a thousand cores. If your work can be broken down into a thousand peices that can be computed independently, than using a GPU can get you ludicrous speedups.
- lucaspiller 11y ago
  This is a bit of an over simplification, because the 'cores' of a GPU aren't really comparable to a CPU core. It's more like scaling up a factory - at a single point on the production like you go from 1 person doing a job to 100 people doing the same job with the same tools. It means you can do more of the same thing, but can't do 100 completely different things. Here is a good overview: http://gamedev.stackexchange.com/a/17255 http://gamedev.stackexchange.com/a/17255
- bogomipz 11y ago
  Thanks
deleted 11y ago
[deleted]
SixSigma 11y ago
I think it is something people just like to say because both sound a big magical. Neural nets are matrix multiplications and addition. They are generally memory constrained on GPUs and Main <> GPU memory is also a bottleneck. I don't see how an FPGA will really help there.
- mtgx 11y ago

deleted 11y ago

[deleted]