5 ms·

Our company works on this kind of stuff for those who are interested (http://isosemi.com http://isosemi.com) We're seeing more like 10-100x improvements in en

Our company works on this kind of stuff for those who are interested (http://isosemi.com http://isosemi.com) We're seeing more like 10-100x improvements in energy efficiency and performance, not 10000x, unless the comparison point is a full blown CPU/GPU.

nickpsecurity 11y ago
I was recently digging into analog papers to try to figure out how to apply it more general-purpose or at least tap into it for special purpose functions. I'm not hardware trained so much as a systems guy who knows enough to give others tips on what to look into. Here's some links I discovered: https://www.schneier.com/blog/archives/2015/07/friday_squid_bl_488.html#c6701962 https://www.schneier.com/blog/archives/2015/07/friday_squid_... Accidentally running into another group using analog selectively for acceleration is pretty neat. The coprocessor was a believable improvement showing analog power. What's your thoughts on the computing with free space and no transistors stuff? Do those other links come off as bogus to a pro or plausible enough to encourage local college students to try something with it? I think there's vast untapped potential in shifting certain functions back to analog and improving the integration of the two. Maybe in general-purpose, too. Almost certainly in INFOSEC w/ analog supporting obfuscation and tamper-detection.
- Quanticles 11y ago
  There is a lot of interesting research out there on analog computing, analog neural networks, and far-out stuff like transistor-free computing. Going from research project to product on Digikey is a really huge leap for most research though. Designing a chip is very expensive, so the product better be a slam dunk. Most of these analog neural network projects can do some sort of learning with small black and white patterns, which does not approach the accuracy or scale of software neural networks. What we're working on is an accelerator for the convolutional neural networks that are winning competitions like ILSVRC. Even that by itself is insufficient for a business case, though. You also have to have end application in mind too, and that end application better be power intensive or performance constrained enough that software cannot accomplish what you need it to do. Because, if software is good enough, then why take a risk on a fancy new hardware component?
  - nickpsecurity 11y ago
    That sounds like a practical application. Good to see a company using analog for what's mostly an analog architecture (neural). Certain parts are easily modelled with digital circuits. Certain parts could benefit from continuous, simple, parallel processing. An analog domain. I'm sure it's tricky to find the right split and integration scheme esp if you're targeting CNN's like I read about here. Good luck on that as I'm sure it will make similarly interesting reading and potentially a useful product if I need CNN's. :) "Because, if software is good enough, then why take a risk on a fancy new hardware component?" Good point. Something that's done in many. Gotta have a clear benefit esp in price/performance/energy. This market has almost as many shut-downs as start-ups.
  - p1esk 11y ago
    Right now, software (GPU based) implementations of neural networks are acceptable because the models are constantly changing. Whatever you build in hardware today will be obsolete in a year (unless your hw is flexible enough, but then it loses a lot of its efficiency, and GPUs will probably catch up with you soon). However, as we discover more algorithms for general intelligence, we will reach a point where the model can learn on its own - just like a human baby does. That will be the point where we will need size, speed, and power efficiency, rather than flexibility. That will be a good moment to offer a hardware solution, and that's when an analog chip will suddenly become more attractive than a digital one.
    - Quanticles 11y ago
      The products that we are creating are reprogrammable and reconfigurable, just like a GPU or FPGA. Updates are like a firmware update. Our hardware would be no more obsolete over time than a GPU or CPU running in its place, and given the huge improvements over CPU/GPU, it would be many years before CPU/GPU would catch up to any particular product anyway. They are not able learn on chip - that is a non-starter and not particularly useful anyway. Customers dont want self-driving cars that need to learn how to drive, they want self-driving cars that already know how to drive.
      - 11y ago
minthd 11y ago
The 10000x improvement is improvement in power * size. So you might be on the same ballpark. What abut large scale neural networks - they aren't mentioned in your site . No plans for that ?
- Quanticles 11y ago
  Target applications like self-driving cars would require deep convolutional neural networks like NVIDIA's Drive PX

nickpsecurity 11y ago

I was recently digging into analog papers to try to figure out how to apply it more general-purpose or at least tap into it for special purpose functions. I'm not hardware trained so much as a systems guy who knows enough to give others tips on what to look into. Here's some links I discovered: https://www.schneier.com/blog/archives/2015/07/friday_squid_bl_488.html#c6701962 https://www.schneier.com/blog/archives/2015/07/friday_squid_... Accidentally running into another group using analog selectively for acceleration is pretty neat. The coprocessor was a believable improvement showing analog power. What's your thoughts on the computing with free space and no transistors stuff? Do those other links come off as bogus to a pro or plausible enough to encourage local college students to try something with it? I think there's vast untapped potential in shifting certain functions back to analog and improving the integration of the two. Maybe in general-purpose, too. Almost certainly in INFOSEC w/ analog supporting obfuscation and tamper-detection.

Quanticles 11y ago
There is a lot of interesting research out there on analog computing, analog neural networks, and far-out stuff like transistor-free computing. Going from research project to product on Digikey is a really huge leap for most research though. Designing a chip is very expensive, so the product better be a slam dunk. Most of these analog neural network projects can do some sort of learning with small black and white patterns, which does not approach the accuracy or scale of software neural networks. What we're working on is an accelerator for the convolutional neural networks that are winning competitions like ILSVRC. Even that by itself is insufficient for a business case, though. You also have to have end application in mind too, and that end application better be power intensive or performance constrained enough that software cannot accomplish what you need it to do. Because, if software is good enough, then why take a risk on a fancy new hardware component?
- nickpsecurity 11y ago
  That sounds like a practical application. Good to see a company using analog for what's mostly an analog architecture (neural). Certain parts are easily modelled with digital circuits. Certain parts could benefit from continuous, simple, parallel processing. An analog domain. I'm sure it's tricky to find the right split and integration scheme esp if you're targeting CNN's like I read about here. Good luck on that as I'm sure it will make similarly interesting reading and potentially a useful product if I need CNN's. :) "Because, if software is good enough, then why take a risk on a fancy new hardware component?" Good point. Something that's done in many. Gotta have a clear benefit esp in price/performance/energy. This market has almost as many shut-downs as start-ups.
- p1esk 11y ago
  Right now, software (GPU based) implementations of neural networks are acceptable because the models are constantly changing. Whatever you build in hardware today will be obsolete in a year (unless your hw is flexible enough, but then it loses a lot of its efficiency, and GPUs will probably catch up with you soon). However, as we discover more algorithms for general intelligence, we will reach a point where the model can learn on its own - just like a human baby does. That will be the point where we will need size, speed, and power efficiency, rather than flexibility. That will be a good moment to offer a hardware solution, and that's when an analog chip will suddenly become more attractive than a digital one.
  - Quanticles 11y ago
    The products that we are creating are reprogrammable and reconfigurable, just like a GPU or FPGA. Updates are like a firmware update. Our hardware would be no more obsolete over time than a GPU or CPU running in its place, and given the huge improvements over CPU/GPU, it would be many years before CPU/GPU would catch up to any particular product anyway. They are not able learn on chip - that is a non-starter and not particularly useful anyway. Customers dont want self-driving cars that need to learn how to drive, they want self-driving cars that already know how to drive.
    - 11y ago

minthd 11y ago

The 10000x improvement is improvement in power * size. So you might be on the same ballpark. What abut large scale neural networks - they aren't mentioned in your site . No plans for that ?

Quanticles 11y ago
Target applications like self-driving cars would require deep convolutional neural networks like NVIDIA's Drive PX