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Hardware Efficient Machine Learning

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If you have a question about this talk, please contact Alessandro Davide Ialongo.

Abstract

Since machine learning is becoming more and more relevant in daily applications, especially in non-virtual environments such as self-driving cars and embedded systems, energy and hardware efficient machine learning approaches are an important and quickly emerging topic. In this reading group we discuss several recent advances in this field.

Recommended Reading

  • I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, Y. Bengio, Binarized Neural Networks, NIPS , 2016.
  • Anonymous, Discrete-Valued Neural Networks using Variational Inference, submitted to ICLR , 2018.
  • C. Louizos, K. Ullrich, M. Welling, Bayesian Compression for Deep Learning, NIPS 2017 .

This talk is part of the Machine Learning Reading Group @ CUED series.

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