Implementation of efficient, low power deep neural networks on next-generation intel client platforms

Michael Deisher; Andrzej Polonski

ICASSP 2017accepted0 citations

Implementation of efficient, low power deep neural networks on next-generation intel client platforms

Abstract

In recent years many signal processing applications involving classification, detection, and inference have enjoyed substantial accuracy improvements due to advances in deep learning. At the same time, the “Internet of Things” has become an important class of devices. Although the paradigm of local sensing and remote inference has been very successful (e.g., Apple Siri, Google Now, Microsoft Cortana, Amazon Alexa, and others) there exist many valuable applications where sensing duration is very long, the cost of communication is high, and scaling to millions or billions of devices is not practical. In such cases, local inference “at the edge” is attractive provided it can be done without compromising accuracy and within the thermal envelope and expected battery life of the edge device.

BibTeX

@inproceedings{icassp2017_implementationof,
  title = {Implementation of efficient, low power deep neural networks on next-generation intel client platforms},
  author = {Michael Deisher and Andrzej Polonski},
  booktitle = {ICASSP 2017},
  year = {2017}
}