Detail publikace

Improving the Accuracy and Hardware Efficiency of Neural Networks Using Approximate Multipliers

ANSARI, M. MRÁZEK, V. COCKBURN, B. SEKANINA, L. VAŠÍČEK, Z. HAN, J.

Originální název

Improving the Accuracy and Hardware Efficiency of Neural Networks Using Approximate Multipliers

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

Improving the accuracy of a neural network (NN) usually requires using larger hardware that consumes more energy. However, the error tolerance of NNs and their applications allow approximate computing techniques to be applied to reduce implementation costs. Given that multiplication is the most resource-intensive and power-hungry operation in NNs, more economical approximate multipliers (AMs) can significantly reduce hardware costs. In this article, we show that using AMs can also improve the NN accuracy by introducing noise. We consider two categories of AMs: 1) deliberately designed and 2) Cartesian genetic programing (CGP)-based AMs. The exact multipliers in two representative NNs, a multilayer perceptron (MLP) and a convolutional NN (CNN), are replaced with approximate designs to evaluate their effect on the classification accuracy of the Mixed National Institute of Standards and Technology (MNIST) and Street View House Numbers (SVHN) data sets, respectively. Interestingly, up to 0.63% improvement in the classification accuracy is achieved with reductions of 71.45% and 61.55% in the energy consumption and area, respectively. Finally, the features in an AM are identified that tend to make one design outperform others with respect to NN accuracy. Those features are then used to train a predictor that indicates how well an AM is likely to work in an NN.

Klíčová slova

approximate multipliers, Cartesian genetic programming, convolutional neural network, multi-layer perceptron, neural networks

Autoři

ANSARI, M.; MRÁZEK, V.; COCKBURN, B.; SEKANINA, L.; VAŠÍČEK, Z.; HAN, J.

Vydáno

22. 1. 2020

ISSN

1063-8210

Periodikum

IEEE Trans. on VLSI Systems.

Ročník

28

Číslo

2

Stát

Spojené státy americké

Strany od

317

Strany do

328

Strany počet

12

URL

BibTex

@article{BUT161464,
  author="ANSARI, M. and MRÁZEK, V. and COCKBURN, B. and SEKANINA, L. and VAŠÍČEK, Z. and HAN, J.",
  title="Improving the Accuracy and Hardware Efficiency of Neural Networks Using Approximate Multipliers",
  journal="IEEE Trans. on VLSI Systems.",
  year="2020",
  volume="28",
  number="2",
  pages="317--328",
  doi="10.1109/TVLSI.2019.2940943",
  issn="1063-8210",
  url="https://www.fit.vut.cz/research/publication/12066/"
}