Accelerating Deep Neural Networks on Low Power Heterogeneous Architectures

@inproceedings{Loukadakis2018AcceleratingDN,
  title={Accelerating Deep Neural Networks on Low Power Heterogeneous Architectures},
  author={Manolis Loukadakis and Jos{\'e} Cano and Michael F. P. O’Boyle},
  year={2018}
}
Deep learning applications are able to recognise images and speech with great accuracy, and their use is now everywhere in our daily lives. However, developing deep learning architectures such as deep neural networks in embedded systems is a challenging task because of the demanding computational resources and power consumption. Hence, sophisticated algorithms and methods that exploit the hardware of the embedded systems need to be investigated. This paper is our first step towards examining… CONTINUE READING

References

Publications referenced by this paper.
Showing 1-10 of 26 references

Similar Papers

Loading similar papers…