Reliable and Robust RRAM-based Neuromorphic Computing
Reliable and Robust RRAM-based Neuromorphic Computing
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DOI：
10.1145/3386263.3407579
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发表时间：
2020-09
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通讯作者：
Grace Li Zhang;Bing Li;Ying Zhu;Shuhang Zhang;Tianchen Wang;Yiyu Shi;Tsung-Yi Ho;Hai Li;Ulf Schlichtmann
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文献类型：
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作者：
Grace Li Zhang;Bing Li;Ying Zhu;Shuhang Zhang;Tianchen Wang;Yiyu Shi;Tsung-Yi Ho;Hai Li;Ulf Schlichtmann
基于 RRAM 的交叉开关是一种很有前景的硬件平台，可加速神经网络的计算。在这种交叉开关用作神经网络的加速器之前，RRAM 单元应该被编程为目标电阻来表示神经网络中的权重。然而，此过程会降低 RRAM 单元电阻的有效范围（从新鲜状态开始），称为老化效应。因此，经过一定次数的编程迭代后，这些 RRAM 单元无法再可靠地编程，从而对神经网络的分类精度产生负面影响。此外，制造过程中的工艺变化以及 RRAM 单元编程过程中的噪声也会导致精度显着下降。为了解决上述问题，在本文中，我们引入了一种软件/硬件协同设计框架来减少 RRAM 交叉开关的老化效应。为了应对过程变化和噪声，我们首先将它们建模为随机变量，然后考虑这些变量修改软件训练中的计算。仿真结果表明，采用协同设计框架，RRAM Crossbar的寿命可延长多达11倍，并且在工艺变化和噪声下的推理精度的平均值和标准差可显着提高。
RRAM-based crossbars are a promising hardware platform to accelerate computations in neural networks. Before such a crossbar can be used as an accelerator for neural networks, RRAM cells should be programmed to target resistances to represent weights in neural networks. However, this process degrades the valid range of the resistances of RRAM cells from the fresh state, called aging effect. Therefore, after a certain number of programming iterations, these RRAM cells cannot be programmed reliably anymore, affecting the classification accuracy of neural networks negatively. In addition, process variations during manufacturing and noise during programming of RRAM cells also lead to significant accuracy degradation. To solve the problems described above, in this paper, we introduce a software/hardware codesign framework to reduce the aging effect in RRAM crossbars. To counter process variations and noise, we first model them as random variables and then modify the computations in software training considering these variables. Simulation results show that the lifetime of RRAM crossbars can be extended by up to 11 times with the codesign framework and the mean value and the standard deviation of the inference accuracy under process variations and noise can be improved significantly.