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Resistive Random Access Memory (RRAM) is an emerging device forprocessing-in-memory (PIM) architecture to accelerate convolutional neuralnetwork (CNN). However, due to the highly coupled crossbar structure in theRRAM array, it is difficult to exploit the sparsity of the network inRRAM-based CNN accelerator. To optimize the weight mapping of sparse network inthe RRAM array and achieve high area and energy efficiency, we propose a novelweight mapping scheme and corresponding RRAM-based CNN accelerator architecturebased on pattern pruning and Operation Unit(OU) mechanism. Experimental resultsshow that our work can achieve 4.16x-5.20x crossbar area efficiency,1.98x-2.15x energy efficiency, and 1.15x-1.35x performance speedup incomparison with the traditional weight mapping method.

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