Sparseness Ratio Allocation and Neuron Re-pruning for Neural Networks Compression

Convolutional neural networks (CNNs) are rapidly gaining popularity in artificial intelligence applications and employed in mobile devices. However, this is challenging because of the high computational complexity of CNNs and the limited hardware resource in mobile devices. To address this issue, compressing the CNN model is an efficient solution. This work presents a new framework of model compression, with the sparseness ratio allocation (SRA) and the neuron re-pruning (NRP). To achieve a higher overall spareness ratio, SRA is exploited to determine pruned weight percentage for each layer. NRP is performed after the usual weight pruning to further reduce the relative redundant neurons in the meanwhile of guaranteeing the accuracy. From experimental results, with a slight accuracy drop of 0.1%, the proposed framework achieves 149.3 x compression on lenet-5. The storage size can be reduced by about 50% relative to previous works. 8-45.2% computational energy and 11.5-48.2% memory traffic energy are saved.