Low-complexity Approximate Convolutional Neural Networks

• URL: http://arxiv.org/abs/2208.00087v1
• Date: Fri, 29 Jul 2022 21:59:29 GMT
• Title: Low-complexity Approximate Convolutional Neural Networks
• Authors: R. J. Cintra, S. Duffner, C. Garcia, A. Leite
• Abstract summary: We present an approach for minimizing the computational complexity of trained Convolutional Neural Networks (ConvNet) The idea is to approximate all elements of a given ConvNet with efficient approximations capable of extreme reductions in computational complexity. Such low-complexity structures pave the way for low-power, efficient hardware designs.
• Score: 1.7368964547487395
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present an approach for minimizing the computational complexity of trained Convolutional Neural Networks (ConvNet). The idea is to approximate all elements 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 1000 parameters); (ii) another one for hand-written digit classification (with more than 180000 parameters); and (iii) a significantly larger ConvNet: AlexNet with $\approx$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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