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My first intuitive thought is "gosh, please, don't". It sounds like a crazy idea to integrate a neural net to everything - it might work in the 99.999% cases, b

by d33 5y ago

My first intuitive thought is "gosh, please, don't". It sounds like a crazy idea to integrate a neural net to everything - it might work in the 99.999% cases, but when something like this fails, good luck debugging it, re-training the network and then verifying if your improvement helped on a tight schedule. And it's also a dangerous architectural paradigm - will we end up with syscalls deciding which side effects happen deciding on whether the neural network things the file is going to be useful in the future? I don't argument for excess simplicity, but if you can't explain (critical) behavior of your code without referring to a big matrix of NN weights, it's probably a bad idea.

magicalhippo 5y ago
As I noted in my previous comment I had a similar gut reaction. However reading the paper, for me the most interesting conclusion was this: Thus, this paper has shown how we can use deep learning in an offline setting to derive insights that lead to an improved set of features with which to make predictions for cache replacement. More broadly, our approach in designing Glider suggests that deep learning can play an important role in systematically exploring features and feature representations that can improve the effectiveness of much simpler models, such as perceptrons and SVMs.
- signa11 5y ago
  does the kernel now support floating point operations ?
  - magicalhippo 5y ago
    I'm no kernel developer so I don't know. The dataset they used for training the neural net was a recorded dataset, so training could be done offline. The specific predictor they created was integer based, so could be used in a non-floating point kernel: We then use an SVM with the k-sparse binary feature. Since integer operations are much cheaper in hardware than floating point operations, we use an Integer SVM (ISVM) with an integer margin and learning rate of 1. Again this highlights the interesting point of the paper, IMHO.

by d33 5y ago

magicalhippo 5y ago

As I noted in my previous comment I had a similar gut reaction. However reading the paper, for me the most interesting conclusion was this: Thus, this paper has shown how we can use deep learning in an offline setting to derive insights that lead to an improved set of features with which to make predictions for cache replacement. More broadly, our approach in designing Glider suggests that deep learning can play an important role in systematically exploring features and feature representations that can improve the effectiveness of much simpler models, such as perceptrons and SVMs.

signa11 5y ago
does the kernel now support floating point operations ?
- magicalhippo 5y ago
  I'm no kernel developer so I don't know. The dataset they used for training the neural net was a recorded dataset, so training could be done offline. The specific predictor they created was integer based, so could be used in a non-floating point kernel: We then use an SVM with the k-sparse binary feature. Since integer operations are much cheaper in hardware than floating point operations, we use an Integer SVM (ISVM) with an integer margin and learning rate of 1. Again this highlights the interesting point of the paper, IMHO.