High Area/Energy Efficiency RRAM CNN Accelerator with Pattern-Pruning-Based Weight Mapping Scheme

Resistive random access memory (RRAM) is an emerging device for processing-in-memory (PIM) architecture to accelerate convolutional neural network (CNN). However, due to the highly coupled crossbar structure in the RRAM array, it is difficult to exploit the CNN sparsity feature to improve the performance in RRAM-based CNN accelerator. To optimize the weight mapping of sparse network in the RRAM array and improve area and energy efficiency, we propose a novel weight mapping scheme and corresponding RRAM-based CNN accelerator architecture based on pattern pruning and operation unit(OU) mechanism. Experimental results show that our work can achieve 4.16x-5.20x crossbar area efficiency, 1.98x2.15x energy efficiency, and 1.15x-1.35x performance speedup in comparison with the traditional method.