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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 resea

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.
  - nickpsecurity 11y ago
    "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." That's a good point to not overlook. Plus, you mentioning this just gave me an idea for a Triad Semiconductor-style, via/metal-programmable, CNN chip tied to a specific FPGA architecture for easy prototyping and conversion. Could be some promise in there. Brain hasn't gotten further than that sentence so don't ask for details haha. Not clear to me how you will do analog and reprogrammable at the same time unless your reprogramming is building things around the analog components that still perform pretty much the same function(s). I could see the weights, connections, location on chip, etc being configured while connected to analog, signal processing blocks scattered throughout chip kind of like FPGA's do with MAC's. My guess as a non-HW guy with a little research into these things. Am I anywhere close?
    - Quanticles 11y ago
      We make use of non-volatile memories throughout the chip, which stores the configuration and weights
      - nickpsecurity 11y ago
        I figured. I was talking more on how you mix analog and digital parts. Do you reconfigure analog like field programmable analog arrays do? Or do you use the same analog circuits while modifying digital part go just put different things through them? Trying to see if there's a consensus emerging in how people accelerate w/ mixed-signal chips. Might help academics figure out better place to start on next project.

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.
- nickpsecurity 11y ago
  "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." That's a good point to not overlook. Plus, you mentioning this just gave me an idea for a Triad Semiconductor-style, via/metal-programmable, CNN chip tied to a specific FPGA architecture for easy prototyping and conversion. Could be some promise in there. Brain hasn't gotten further than that sentence so don't ask for details haha. Not clear to me how you will do analog and reprogrammable at the same time unless your reprogramming is building things around the analog components that still perform pretty much the same function(s). I could see the weights, connections, location on chip, etc being configured while connected to analog, signal processing blocks scattered throughout chip kind of like FPGA's do with MAC's. My guess as a non-HW guy with a little research into these things. Am I anywhere close?
  - Quanticles 11y ago
    We make use of non-volatile memories throughout the chip, which stores the configuration and weights
    - nickpsecurity 11y ago
      I figured. I was talking more on how you mix analog and digital parts. Do you reconfigure analog like field programmable analog arrays do? Or do you use the same analog circuits while modifying digital part go just put different things through them? Trying to see if there's a consensus emerging in how people accelerate w/ mixed-signal chips. Might help academics figure out better place to start on next project.