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%.

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Supervisor:Matthias Kissel

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Workflow

Start

  • Topic specification
  • Definition of work packages
  • Composition of a project proposal and time plan
  • Project Talk with Prof. Diepold
  • Registration of the thesis
  • Creation of a wiki page (supervisor)
  • Creation of a gitlab repository or branch
  • Access to lab and computers

Finalization

  • Check code base and data
  • Check documentation¬†
  • Provide an example notebook that describes the workflow/usage of your code (in your repo)
  • Proof read written composition
  • Rehearsal presentation
  • Submission of written composition
  • Submission of presentation¬†
  • Recording of presentation / Presentation in group meeting
  • Final Presentation