Tight Compression: Compressing CNN Through Fine-Grained Pruning and Weight Permutation for Efficient Implementation

TL;DR

A model compression method based on a novel weight permutation scheme to fully exploit the fine-grained weight sparsity in the hardware design and two pruning granularities are explored.

Abstract

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 5.88x to 14.13x. As a result, the throughput and energy efficiency are improved by 2.75 and 1.86 times, respectively.

Figures (13)

• Figure 1: CNN Model and the MAC Operation at One Neuron.
• Figure 2: (a) Matrix Multiplication in the Systolic Array and (b) the Conflict Pruning-Based Compression PackCNN.
• Figure 3: An Illustrating Example of Weight Permutation.
• Figure 4: Compression Result of a Weight Matrix with 2 Row Sections.
• Figure 5: The Energy Difference ($E'-E$) for SA.