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arXiv · 2303.14470

Compacting Binary Neural Networks by Sparse Kernel Selection

Yikai Wang·Wenbing Huang·Yinpeng Dong·Fuchun Sun·Anbang Yao
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Abstract

Binary Neural Network (BNN) represents convolution weights with 1-bit values, which enhances the efficiency of storage and computation. This paper is motivated by a previously revealed phenomenon that the binary kernels in successful BNNs are nearly power-law distributed: their values are mostly clustered into a small number of codewords. This phenomenon encourages us to compact typical BNNs and obtain further close performance through learning non-repetitive kernels within a binary kernel subspace. Specifically, we regard the binarization process as kernel grouping in terms of a binary codebook, and our task lies in learning to select a smaller subset of codewords from the full codebook. We then leverage the Gumbel-Sinkhorn technique to approximate the codeword selection process, and develop the Permutation Straight-Through Estimator (PSTE) that is able to not only optimize the selection process end-to-end but also maintain the non-repetitive occupancy of selected codewords. Experiments verify that our method reduces both the model size and bit-wise computational costs, and achieves accuracy improvements compared with state-of-the-art BNNs under comparable budgets.

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Yikai Wang, Wenbing Huang, Yinpeng Dong, Fuchun Sun, Anbang Yao. 2023-03-25. Compacting Binary Neural Networks by Sparse Kernel Selection. https://arxiv.org/abs/2303.14470

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