Detail publikace

Design of Power-Efficient Approximate Multipliers for Approximate Artificial Neural Networks

MRÁZEK, V. SARWAR, S. SEKANINA, L. VAŠÍČEK, Z. ROY, K.

Originální název

Design of Power-Efficient Approximate Multipliers for Approximate Artificial Neural Networks

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

Artificial neural networks (NN) have shown a significant promise in difficult tasks like image classification or speech recognition. Even well-optimized hardware implementations of digital NNs show significant power consumption. It is mainly due to non-uniform pipeline structures and inherent redundancy of numerous arithmetic operations that have to be performed to produce each single output vector.  This paper provides a methodology for the design of well-optimized power-efficient NNs with a uniform structure suitable for hardware implementation. An error resilience analysis was performed in order to determine key constraints for the design of approximate multipliers that are employed in the resulting structure of NN. By means of a search based approximation method, approximate multipliers showing desired tradeoffs between the accuracy and implementation cost were created. Resulting approximate NNs, containing the approximate multipliers, were evaluated using standard benchmarks (MNIST dataset) and a real-world classification problem of Street-View House Numbers. Significant improvement in power efficiency was obtained in both cases with respect to regular NNs. In some cases, 91% power reduction of multiplication led to classification accuracy degradation of less than 2.80%. Moreover, the paper showed the capability of the back propagation learning algorithm to adapt with NNs containing the approximate multipliers. 

Klíčová slova

Approximate computing, Neural networks, Logic synthesis, Low power, Genetic programming

Autoři

MRÁZEK, V.; SARWAR, S.; SEKANINA, L.; VAŠÍČEK, Z.; ROY, K.

Vydáno

7. 9. 2016

Nakladatel

Association for Computing Machinery

Místo

Austin, TX

ISBN

978-1-4503-4466-1

Kniha

Proceedings of the IEEE/ACM International Conference on Computer-Aided Design

Strany od

811

Strany do

817

Strany počet

7

URL

https://www.fit.vut.cz/research/publication/11142/

BibTex

@inproceedings{BUT133493,
  author="Vojtěch {Mrázek} and Syed Shakib {Sarwar} and Lukáš {Sekanina} and Zdeněk {Vašíček} and Kaushik {Roy}",
  title="Design of Power-Efficient Approximate Multipliers for Approximate Artificial Neural Networks",
  booktitle="Proceedings of the IEEE/ACM International Conference on Computer-Aided Design",
  year="2016",
  pages="811--817",
  publisher="Association for Computing Machinery",
  address="Austin, TX",
  doi="10.1145/2966986.2967021",
  isbn="978-1-4503-4466-1",
  url="https://www.fit.vut.cz/research/publication/11142/"
}