Energy-efficient hybrid stochastic-binary neural networks for near-sensor computing

@article{Lee2017EnergyefficientHS,
  title={Energy-efficient hybrid stochastic-binary neural networks for near-sensor computing},
  author={Vincent T. Lee and Armin Alaghi and John P. Hayes and Visvesh Sathe and Luis Ceze},
  journal={Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017},
  year={2017},
  pages={13-18}
}
Recent advances in neural networks (NNs) exhibit unprecedented success at transforming large, unstructured data streams into compact higher-level semantic information for tasks such as handwriting recognition, image classification, and speech recognition. Ideally, systems would employ near-sensor computation to execute these tasks at sensor endpoints to maximize data reduction and minimize data movement. However, near-sensor computing presents its own set of challenges such as operating power… CONTINUE READING
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