Learning the Sparsity for ReRAM: Mapping and Pruning Sparse Neural Network for ReRAM based Accelerator

With the in-memory processing ability, ReRAM based computing gets more and more attractive for accelerating neural networks (NNs). However, most ReRAM based accelerators cannot support efficient mapping for sparse NN, and we need to map the whole dense matrix onto ReRAM crossbar array to achieve O(1) computation complexity. In this paper, we propose a sparse NN mapping scheme based on elements clustering to achieve better ReRAM crossbar utilization. Further, we propose crossbar-grained pruning algorithm to remove the crossbars with low utilization. Finally, since most current ReRAM devices cannot achieve high precision, we analyze the effect of quantization precision for sparse NN, and propose to complete high-precision composing in the analog field and design related periphery circuits. In our experiments, we discuss how the system performs with different crossbar sizes to choose the optimized design. Our results show that our mapping scheme for sparse NN with proposed pruning algorithm achieves 3 - 5x energy efficiency and more than 2:5 - 6x speedup, compared with those accelerators for dense NN. Also, the accuracy experiments show that our pruning method appears to have almost no accuracy loss.