In this paper, we present an efficient hardware mapping methodology for realizing vector matrix multiplication (VMM) on resistive memory (RRAM) arrays. Using the proposed VMM computation technique, we experimentally demonstrate a binarized-ADALINE (Adaptive Linear) classifier on an OxRAM crossbar. An 8x8 OxRAM crossbar with Ni/3-nm HfO2/7 nm Al-doped-TiO2/TiN device stack is used. Weight training for the binarized-ADALINE classifier is performed ex-situ on UCI cancer dataset. Post weight generation the OxRAM array is carefully programmed to binary weight-states using the proposed weight mapping technique on a custom-built testbench. Our VMM powered binarized-ADALINE network achieves a classification accuracy of 78% in simulation and 67% in experiments. Experimental accuracy was found to drop mainly due to crossbar inherent sneak-path issues and RRAM device programming variability.

中文翻译：

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使用二进制 RRAM 阵列实现 VMM 的方法：使用 OxRAM 交叉开关的二进制化 ADALINE 的实验演示

在本文中，我们提出了一种有效的硬件映射方法，用于在电阻存储器 (RRAM) 阵列上实现向量矩阵乘法 (VMM)。使用提出的 VMM 计算技术，我们在 OxRAM 交叉开关上通过实验演示了二值化 ADALINE（自适应线性）分类器。使用带有 Ni/3-nm HfO2/7 nm Al 掺杂-TiO2/TiN 器件堆栈的 8x8 OxRAM 交叉开关。二值化 ADALINE 分类器的权重训练是在 UCI 癌症数据集上进行的。权重生成后，OxRAM 阵列被仔细编程为二进制权重状态，在定制的测试平台上使用建议的权重映射技术。我们的 VMM 驱动的二值化 ADALINE 网络在模拟中达到了 78%，在实验中达到了 67%。