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

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Vojtech Mrazek and Syed Shakib Sarwar and 
                 Lukas Sekanina and Zdenek Vasicek and Kaushik Roy",
  title =        "Design of Power-efficient Approximate Multipliers for
                 Approximate Artificial Neural Networks",
  booktitle =    "Proceedings of the 35th International Conference on
                 Computer-Aided Design",
  year =         "2016",
  pages =        "81:1--81:7",
  articleno =    "81",
  address =      "Austin, Texas, USA",
  month =        nov # " 7-10",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming, Approximate computing, Neural
                 networks, Logic synthesis, Low power",
  acmid =        "2967021",
  isbn13 =       "978-1-4503-4466-1",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1145/2966986.2967021",
  size =         "7 pages",
  abstract =     "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, 91percent power reduction
                 of multiplication led to classification accuracy
                 degradation of less than 2.80percent. Moreover, the
                 paper showed the capability of the back propagation
                 learning algorithm to adapt with NNs containing the
                 approximate multipliers.",

Genetic Programming entries for Vojtech Mrazek Syed Shakib Sarwar Lukas Sekanina Zdenek Vasicek Kaushik Roy