Balancing the learning ability and memory demand of a perceptron-based dynamically trainable neural network

  • Edward Richter
  • Spencer Valancius
  • Josiah McClanahan
  • John Mixter
  • Ali Akoglu
Article
First Online:

Abstract

Artificial neural networks (ANNs) have become a popular means of solving complex problems in prediction-based applications such as image and natural language processing. Two challenges prominent in the neural network domain are the practicality of hardware implementation and dynamically training the network. In this study, we address these challenges with a development methodology that balances the hardware footprint and the quality of the ANN. We use the well-known perceptron-based branch prediction problem as a case study for demonstrating this methodology. This problem is perfect to analyze dynamic hardware implementations of ANNs because it exists in hardware and trains dynamically. Using our hierarchical configuration search space exploration, we show that we can decrease the memory footprint of a standard perceptron-based branch predictor by 2.3\(\times \) with only a 0.6% decrease in prediction accuracy.

Keywords

Artificial neural network Branch prediction Perceptron SimpleScalar 
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Notes

Acknowledgements

Research reported in this publication was supported in part by Raytheon Missile Systems under the contract 2017-UNI-0008. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Raytheon Missile Systems.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Edward Richter
    • 1
  • Spencer Valancius
    • 1
  • Josiah McClanahan
    • 1
  • John Mixter
    • 1
  • Ali Akoglu
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of ArizonaTucsonUSA

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