5 ms·

Robustness could have been added as one extra constraint. I.e. the fitness function would also value the ability of the circuit to work on many instances of the

Robustness could have been added as one extra constraint. I.e. the fitness function would also value the ability of the circuit to work on many instances of the FPGA. This reminds me of Physically Unclonable Functions (PUFs) that tries to utilize differences in manufactured devices as a method of distinquishing unique devices. This is used for identification and authentication. There are for example PUFs that use differences in delays between logic blocks in FPGAs to generate unique responses to applied patterns/challenges. See for example: http://en.wikipedia.org/wiki/Physical_unclonable_function http://en.wikipedia.org/wiki/Physical_unclonable_function

TheLoneWolfling 12y ago
Even with that setup you end up with overfitting after a point. I.e. instead of getting something that works on every FPGA of that type, you start getting things that work on the specific FPGAs you provide. The same thing happens in machine learning for things like recognizing photos: after a while your algorithm stops recognizing cars and starts just recognizing those specific photos of cars. Ideally, you want to take a bunch of FPGAs, pull out a random subsample of them only for acceptance testing, and stop evolving the circuit when the performance on the acceptance testing subset starts getting worse.
- Houshalter 12y ago
  Even if it worked on the specific FPGAs you provide, it might not work reliably in different temperatures like the one in the article, or if the noise is slightly further away, etc. There is no way to stop overfitting because it's difficult if not impossible to test it in every possible environment we want it to work under.
  - TheLoneWolfling 12y ago
    There are ways to catch overfitting: https://en.wikipedia.org/wiki/Cross-validation_%28statistics%29 https://en.wikipedia.org/wiki/Cross-validation_%28statistics...
    - Houshalter 12y ago
      That's not really relevant to this. That is for selecting hyper-parameters for statistical models.
      - TheLoneWolfling 12y ago
        I know the article doesn't mention it, but you can use the exact same techniques for preventing overfitting in machine learning.
        Houshalter 12y ago
        That's basically what I said, and this is optimization not machine learning. The problem is the genetic algorithm fits the the specifics of the FPGA and the environment it is optimized in, and doesn't work reliably in other environments or FPGAs.

TheLoneWolfling 12y ago

Even with that setup you end up with overfitting after a point. I.e. instead of getting something that works on every FPGA of that type, you start getting things that work on the specific FPGAs you provide. The same thing happens in machine learning for things like recognizing photos: after a while your algorithm stops recognizing cars and starts just recognizing those specific photos of cars. Ideally, you want to take a bunch of FPGAs, pull out a random subsample of them only for acceptance testing, and stop evolving the circuit when the performance on the acceptance testing subset starts getting worse.

Houshalter 12y ago
Even if it worked on the specific FPGAs you provide, it might not work reliably in different temperatures like the one in the article, or if the noise is slightly further away, etc. There is no way to stop overfitting because it's difficult if not impossible to test it in every possible environment we want it to work under.
- TheLoneWolfling 12y ago
  There are ways to catch overfitting: https://en.wikipedia.org/wiki/Cross-validation_%28statistics%29 https://en.wikipedia.org/wiki/Cross-validation_%28statistics...
  - Houshalter 12y ago
    That's not really relevant to this. That is for selecting hyper-parameters for statistical models.
    - TheLoneWolfling 12y ago
      I know the article doesn't mention it, but you can use the exact same techniques for preventing overfitting in machine learning.
      - Houshalter 12y ago
        That's basically what I said, and this is optimization not machine learning. The problem is the genetic algorithm fits the the specifics of the FPGA and the environment it is optimized in, and doesn't work reliably in other environments or FPGAs.