Real time application of deep learning algorithms is often hindered by high
computational complexity and frequent memory accesses. Network pruning is a
promising technique to solve this problem. However, pruning usually results in
irregular network connections that not only demand extra representation efforts
but also do not fit well on parallel computation. We introduce structured
sparsity at various scales for convolutional neural networks, which are channel
wise, kernel wise and intra kernel strided sparsity. This structured sparsity
is very advantageous for direct computational resource savings on embedded
computers, parallel computing environments and hardware based systems. To
decide the importance of network connections and paths, the proposed method
uses a particle filtering approach. The importance weight of each particle is
assigned by computing the misclassification rate with corresponding
connectivity pattern. The pruned network is re-trained to compensate for the
losses due to pruning. While implementing convolutions as matrix products, we
particularly show that intra kernel strided sparsity with a simple constraint
can significantly reduce the size of kernel and feature map matrices. The
pruned network is finally fixed point optimized with reduced word length
precision. This results in significant reduction in the total storage size
providing advantages for on-chip memory based implementations of deep neural
networks.

使用结构化稀疏技术、粒子滤波方法和固定点优化技术对深度神经网络进行了精简，优化后可在嵌入式计算机和硬件系统上提供更加高效和优化的实现。