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ReNEW: Enhancing Lifetime for ReRAM Crossbar Based Neural Network Accelerators

机译：更新：延长基于ReRAM Crossbar的神经网络加速器的使用寿命

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摘要

With analog current accumulation feature, resistive memory (ReRAM) crossbars are widely studied to accelerate neural network applications. The ReRAM crossbar based accelerators have many advantages over conventional CMOS-based accelerators, such as high performance and energy efficiency. However, due to the limited cell endurance, these accelerators suffer from short programming cycles when weights that stored in ReRAM cells are frequently updated during the neural network training phase. In this paper, by exploiting the wearing out mechanism of ReRAM cell, we propose a novel comprehensive framework, ReNEW, to enhance the lifetime of the ReRAM crossbar based accelerators, particularly for neural network training. Evaluation results show that, our proposed schemes reduce the total effective writes to ReRAM crossbar based accelerators by up to 500.3×, 50.0×, 2.83× and 1.60× over two MLC ReRAM crossbar baselines, one SLC ReRAM crossbar baseline and an SLC ReRAM crossbar design with optimal timing, respectively.
机译：利用模拟电流累积特征，广泛研究电阻存储器（RERAM）跨跨度以加速神经网络应用。基于RERAM横杆的加速器具有与传统CMOS的加速器相比具有许多优点，例如高性能和能量效率。然而，由于细胞耐久性有限，当在神经网络训练阶段经常更新存储在reram小区的重量时，这些加速器患有短编程周期。在本文中，通过利用Reram Cell的佩戴机制，我们提出了一种新的综合框架，更新，提高基于Reram横杆的速度的寿命，特别是神经网络训练。评估结果表明，我们提出的计划将基于Reram横杆的加速器的总有效写入高达500.3×，50.0×，2.83×和1.60×超过两个MLC reram横杆基线，一个SLC reram横杆基线和SLC reram横杆设计分别最佳定时。

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