Student:Julian Lorenz

Abstract: In this paper I propose a new method to shorten the weighted sum computation at each neuron in a neural network without requiring retraining. I sort the weighted sum computation order by the magnitude of the weights. If the activation function shows converging behavior, I stop the weighted sum computation early after it has passed a predetermined stopping threshold. I show how to find the stopping thresholds by statistical analysis of the weighted sum computation in a network. I also
provide an experimental analysis on how the online-pruning method performs in comparison to the normal feed-forward computation. Using my approach, the MAC operations in the tested network can be reduced by 14.1%. This results in a speed improvement of 5.1% while achieving an average R2 score of 99.09%.



Supervisor:Matthias Kissel







  • Topic specification
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  • Project Talk with Prof. Diepold
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  • Check code base and data
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