Low-complexity Approximate Convolutional Neural Networks

Abstract : In this paper, we present an approach for minimizing the computational complexity of trained ConvolutionalNeural Networks (ConvNet). The idea is to approximate allelements of a given ConvNet and replace the original convolutional filters and parameters (pooling and bias coefficients; and activation function) with efficient approximations capable of extreme reductions in computational complexity. Low-complexity convolution filters are obtained through a binary (zero-one) linear programming scheme based on the Frobenius norm over sets of dyadic rationals. The resulting matrices allow for multiplication- free computations requiring only addition and bit-shifting operations. Such low-complexity structures pave the way for low-power, efficient hardware designs. We applied our approach on three use cases of different complexity: (i) a “light” but efficient ConvNet for face detection (with around 1 000 parameters); (ii) another one for hand-written digit classification (with more than 180 000 parameters); and (iii) a significantly larger ConvNet: AlexNet with ≈1.2 million matrices. We evaluated the overall performance on the respective tasks for different levels of approximations. In all considered applications, very low-complexity approximations have been derived maintaining an almost equal classification performance.
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Contributeur : Christophe Garcia <>
Soumis le : vendredi 9 mars 2018 - 00:48:56
Dernière modification le : mardi 17 juillet 2018 - 15:45:37



Renato Cintra, Stefan Duffner, Christophe Garcia, André Leite. Low-complexity Approximate Convolutional Neural Networks. IEEE Transactions on Neural Networks and Learning Systems, IEEE, 2018, 〈10.1109/TNNLS.2018.2815435〉. 〈hal-01727219〉



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