Corpus ID: 209319194

Noisy Machines: Understanding Noisy Neural Networks and Enhancing Robustness to Analog Hardware Errors Using Distillation

@article{Zhou2019NoisyMU,
  title={Noisy Machines: Understanding Noisy Neural Networks and Enhancing Robustness to Analog Hardware Errors Using Distillation},
  author={Chuteng Zhou and Prad Kadambi and Matthew Mattina and Paul N. Whatmough},
  journal={ArXiv},
  year={2019},
  volume={abs/2001.04974}
}
  • Chuteng Zhou, Prad Kadambi, +1 author Paul N. Whatmough
  • Published in ArXiv 2019
  • Computer Science, Mathematics
  • The success of deep learning has brought forth a wave of interest in computer hardware design to better meet the high demands of neural network inference. In particular, analog computing hardware has been heavily motivated specifically for accelerating neural networks, based on either electronic, optical or photonic devices, which may well achieve lower power consumption than conventional digital electronics. However, these proposed analog accelerators suffer from the intrinsic noise generated… CONTINUE READING

    Create an AI-powered research feed to stay up to date with new papers like this posted to ArXiv

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 34 REFERENCES

    Deep learning with coherent nanophotonic circuits

    VIEW 6 EXCERPTS
    HIGHLY INFLUENTIAL

    Analog/Mixed-Signal Hardware Error Modeling for Deep Learning Inference

    VIEW 3 EXCERPTS
    HIGHLY INFLUENTIAL

    On the Information Bottleneck Theory of Deep Learning

    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL

    SGDR: Stochastic Gradient Descent with Warm Restarts

    VIEW 1 EXCERPT
    HIGHLY INFLUENTIAL

    Distilling the Knowledge in a Neural Network

    VIEW 3 EXCERPTS
    HIGHLY INFLUENTIAL

    Adversarially Robust Distillation

    VIEW 2 EXCERPTS

    All-optical machine learning using diffractive deep neural networks

    VIEW 1 EXCERPT