Sequential Encryption of Sparse Neural Networks Toward Optimum Representation of Irregular Sparsity

• URL: http://arxiv.org/abs/2105.01869v1
• Date: Wed, 5 May 2021 05:14:50 GMT
• Title: Sequential Encryption of Sparse Neural Networks Toward Optimum Representation of Irregular Sparsity
• Authors: Baeseong Park, Se Jung Kwon, Dongsoo Lee, Daehwan Oh, Byeongwook Kim, Yongkweon Jeon, Yeonju Ro
• Abstract summary: We study fixed-to-fixed encryption architecture/algorithm to support fine-grained pruning methods. We demonstrate that our proposed compression scheme achieves almost the maximum compression ratio for the Transformer and ResNet-50.
• Score: 9.062897838978955
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Even though fine-grained pruning techniques achieve a high compression ratio, conventional sparsity representations (such as CSR) associated with irregular sparsity degrade parallelism significantly. Practical pruning methods, thus, usually lower pruning rates (by structured pruning) to improve parallelism. In this paper, we study fixed-to-fixed (lossless) encryption architecture/algorithm to support fine-grained pruning methods such that sparse neural networks can be stored in a highly regular structure. We first estimate the maximum compression ratio of encryption-based compression using entropy. Then, as an effort to push the compression ratio to the theoretical maximum (by entropy), we propose a sequential fixed-to-fixed encryption scheme. We demonstrate that our proposed compression scheme achieves almost the maximum compression ratio for the Transformer and ResNet-50 pruned by various fine-grained pruning methods.

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