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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Layerwise Sparse Coding for Pruned Deep Neural Networks with Extreme Compression Ratio

February 1, 2023

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Authors

Xiao Liu

Nanjing University


Wenbin Li

Nanjing University


Jing Huo

Nanjing University


Lili Yao

Nanjing University


Yang Gao

Nanjing University


DOI:

10.1609/aaai.v34i04.5927


Abstract:

Deep neural network compression is important and increasingly developed especially in resource-constrained environments, such as autonomous drones and wearable devices. Basically, we can easily and largely reduce the number of weights of a trained deep model by adopting a widely used model compression technique, e.g., pruning. In this way, two kinds of data are usually preserved for this compressed model, i.e., non-zero weights and meta-data, where meta-data is employed to help encode and decode these non-zero weights. Although we can obtain an ideally small number of non-zero weights through pruning, existing sparse matrix coding methods still need a much larger amount of meta-data (may several times larger than non-zero weights), which will be a severe bottleneck of the deploying of very deep models. To tackle this issue, we propose a layerwise sparse coding (LSC) method to maximize the compression ratio by extremely reducing the amount of meta-data. We first divide a sparse matrix into multiple small blocks and remove zero blocks, and then propose a novel signed relative index (SRI) algorithm to encode the remaining non-zero blocks (with much less meta-data). In addition, the proposed LSC performs parallel matrix multiplication without full decoding, while traditional methods cannot. Through extensive experiments, we demonstrate that LSC achieves substantial gains in pruned DNN compression (e.g., 51.03x compression ratio on ADMM-Lenet) and inference computation (i.e., time reduction and extremely less memory bandwidth), over state-of-the-art baselines.

Topics: AAAI

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HOW TO CITE:

Xiao Liu||Wenbin Li||Jing Huo||Lili Yao||Yang Gao Layerwise Sparse Coding for Pruned Deep Neural Networks with Extreme Compression Ratio Proceedings of the AAAI Conference on Artificial Intelligence (2020) 4900-4907.

Xiao Liu||Wenbin Li||Jing Huo||Lili Yao||Yang Gao Layerwise Sparse Coding for Pruned Deep Neural Networks with Extreme Compression Ratio AAAI 2020, 4900-4907.

Xiao Liu||Wenbin Li||Jing Huo||Lili Yao||Yang Gao (2020). Layerwise Sparse Coding for Pruned Deep Neural Networks with Extreme Compression Ratio. Proceedings of the AAAI Conference on Artificial Intelligence, 4900-4907.

Xiao Liu||Wenbin Li||Jing Huo||Lili Yao||Yang Gao. Layerwise Sparse Coding for Pruned Deep Neural Networks with Extreme Compression Ratio. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.4900-4907.

Xiao Liu||Wenbin Li||Jing Huo||Lili Yao||Yang Gao. 2020. Layerwise Sparse Coding for Pruned Deep Neural Networks with Extreme Compression Ratio. "Proceedings of the AAAI Conference on Artificial Intelligence". 4900-4907.

Xiao Liu||Wenbin Li||Jing Huo||Lili Yao||Yang Gao. (2020) "Layerwise Sparse Coding for Pruned Deep Neural Networks with Extreme Compression Ratio", Proceedings of the AAAI Conference on Artificial Intelligence, p.4900-4907

Xiao Liu||Wenbin Li||Jing Huo||Lili Yao||Yang Gao, "Layerwise Sparse Coding for Pruned Deep Neural Networks with Extreme Compression Ratio", AAAI, p.4900-4907, 2020.

Xiao Liu||Wenbin Li||Jing Huo||Lili Yao||Yang Gao. "Layerwise Sparse Coding for Pruned Deep Neural Networks with Extreme Compression Ratio". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.4900-4907.

Xiao Liu||Wenbin Li||Jing Huo||Lili Yao||Yang Gao. "Layerwise Sparse Coding for Pruned Deep Neural Networks with Extreme Compression Ratio". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 4900-4907.

Xiao Liu||Wenbin Li||Jing Huo||Lili Yao||Yang Gao. Layerwise Sparse Coding for Pruned Deep Neural Networks with Extreme Compression Ratio. AAAI[Internet]. 2020[cited 2023]; 4900-4907.


ISSN: 2374-3468


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