论文标题
高面积/能效RRAM CNN加速器具有基于图案修剪的内核重量映射方案
High Area/Energy Efficiency RRAM CNN Accelerator with Kernel-Reordering Weight Mapping Scheme Based on Pattern Pruning
论文作者
论文摘要
电阻随机访问存储器（RRAM）是用于加快卷积神经网络（CNN）的内存（PIM）架构的新兴设备。但是，由于RRAM阵列中高度耦合的横梁结构，很难利用基于RRAM的CNN加速器中网络的稀疏性。为了优化RRAM阵列中稀疏网络的重量映射并实现高面积和能源效率，我们提出了一种新型的重量映射方案，并基于基于模式修剪和操作单元（OU）机制的相应的基于RRAM的CNN加速器体系结构。实验结果表明，与传统的重量映射方法相比，我们的工作可以实现4.16 x-5.20x横杆面积效率，1.98x-2.15x的能源效率和1.15x-1.35x的性能加速。
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 sparsity of the network in RRAM-based CNN accelerator. To optimize the weight mapping of sparse network in the RRAM array and achieve high 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, 1.98x-2.15x energy efficiency, and 1.15x-1.35x performance speedup in comparison with the traditional weight mapping method.
