Exploiting Weight-Level Sparsity in Channel Pruning with Low-Rank Approximation

Acceleration and compression on Deep Neural Networks (DNNs) have become a critical problem to develop intelligence on resource-constrained hardware, especially on Internet of Things (IoT) devices. Previous works based on channel pruning can be easily deployed and accelerated without specialized hardware and software. However, weight-level sparsity is not well explored in channel pruning, which results in relatively low compression rate. In this work, we propose a framework that combines channel pruning with low-rank decomposition to tackle this problem. First, the low-rank decomposition is utilized to eliminate redundancy within filter, and achieves acceleration in shallow layers. Then, we apply channel pruning on the decomposed network in a global way, and obtains further acceleration in deep layers. In addition, a spectral norm-based indicator is proposed to balance low-rank approximation and channel pruning. We conduct a series of ablation experiments and prove that low-rank decomposition can effectively improve channel pruning by generating small and compact filters. To further demonstrate the hardware compatibility, we deploy the pruned networks on the FPGA, and the networks produced by our method have obviously low latency.