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Computation-oriented fault-tolerance schemes for RRAM computing systems

机译:RRAM计算系统的面向计算的容错方案

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The emerging metal-oxide resistive switching random-access memory (RRAM) devices and RRAM crossbar arrays have demonstrated their potential in enormously boosting the speed and energy-efficiency of analog matrix-vector multiplication. Unfortunately, due to the immature fabrication technology, commonly occurring Stuck-At-Faults (SAFs) seriously degrade the computational accuracy of RRAM crossbar based Computing System (RCS). In this paper, we propose a Mapping Algorithm with inner fault-tolerant ability (MAO) to convert matrix parameters into RRAM conductances in RCS by providing larger mapping space and fully exploring the available mapping space. Furthermore, we present two computation-oriented redundancy schemes - `Redundant Crossbars' (RX) and `Independent Redundant Columns' (IRC) to alleviate the loss of computational accuracy due to SAFs. RX adds redundant RRAM crossbar arrays and IRC introduces independent redundant RRAM columns to compensate the computational errors brought by SAFs.
机译:新兴的金属氧化物电阻切换随机存取存储器(RRAM)器件和RRAM交叉开关阵列已展示出它们在极大地提高模拟矩阵矢量乘法的速度和能效方面的潜力。不幸的是,由于不成熟的制造技术,经常发生的“卡住故障”(SAF)严重降低了基于RRAM交叉开关的计算系统(RCS)的计算精度。在本文中,我们提出了一种具有内部容错能力(MAO)的映射算法,通过提供更大的映射空间并充分探索可用的映射空间,将矩阵参数转换为RCS中的RRAM电导。此外,我们提出了两种面向计算的冗余方案-“冗余交叉开关”(RX)和“独立冗余列”(IRC),以减轻由于SAF造成的计算精度损失。 RX添加了冗余RRAM交叉开关阵列,IRC引入了独立的冗余RRAM列以补偿SAF带来的计算错误。

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