Tight Compression: Compressing CNN Model Tightly Through Unstructured Pruning and Simulated Annealing Based Permutation

Abstract

The unstructured sparsity after pruning poses a challenge to the efficient implementation of deep learning models in existing regular architectures like systolic arrays. The coarse-grained structured pruning, on the other hand, tends to have higher accuracy loss than unstructured pruning when the pruned models are of the same size. In this work, we propose a compression method based on the unstructured pruning and a novel weight permutation scheme. Through permutation, the sparse weight matrix is further compressed to a small and dense format to make full use of the hardware resources. Compared to the state-of-the-art works, the matrix compression rate is effectively improved from 5.88x to 10.28x. As a result, the throughput and energy efficiency are improved by 2.12 and 1.57 times, respectively.