DOI码：10.1109/TCAD.2022.3178047

所属单位：HZAU & HKUST

发表刊物：IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD，CCF-A类)

关键字：Deep learning, pruning, weight sparsity, neural network compression

摘要：The unstructured sparsity after pruning poses a challenge to the efficient implementation of deep learning models in existing regular architectures like systolic arrays. On the other hand, coarse-grained structured pruning is suitable for implementation in regular architectures but tends to have higher accuracy loss than unstructured pruning when the pruned models are of the same size. In this work, we propose a model compression method based on a novel weight permutation scheme to fully exploit the fine-grained weight sparsity in the hardware design. Through permutation, the optimal arrangement of the weight matrix is obtained, and the sparse weight matrix is further compressed to a small and dense format to make full use of the hardware resources. Two pruning granularities are explored. In addition to the unstructured weight pruning, we also propose a more fine-grained subword-level pruning to further improve the compression performance. Compared to the state-of-the-art works, the matrix compression rate is significantly improved from 5.88× to 14.13×. As a result, the throughput and energy efficiency are improved by 2.75 and 1.86 times, respectively.

备注：中国计算机学会 CCF-A 类

合写作者：Jingyang Zhu,Jingbo Jiang,Chi-Ying Tsui

第一作者：Xizi Chen

论文类型：期刊论文

是否译文：否

发表时间：2023-02-01

收录刊物：SCI

发布期刊链接：https://ieeexplore.ieee.org/document/9781609/metrics#metrics