用二进制RRAM阵列实现VMM的方法学：用OxRAM交叉开关实现二进制ADALINE的实验验证
Methodology for Realizing VMM with Binary RRAM Arrays: Experimental Demonstration of Binarized-ADALINE Using OxRAM Crossbar
摘要：
提出了一种在电阻存储器阵列上实现向量矩阵乘法的有效硬件映射方法。使用建议的VMM计算技术，我们实验证明了二进制ADALINE（自适应线性）分类器上的OxRAM交叉开关。使用具有Ni/3-nm HfO 2/7 nm Al掺杂的TiO 2/TiN器件叠层的8 × 8 OxRAM交叉杆。在UCI癌症数据集上非原位执行二值化ADALINE分类器的权重训练。后权重生成的OxRAM阵列仔细编程二进制权重状态使用建议的权重映射技术在定制的测试台。我们的VMM供电二进制ADALINE网络实现了78%的模拟和67%的实验中的分类准确率。实验精度下降主要是由于交叉开关固有的潜路径问题和RRAM器件编程的可变性。
英文摘要：
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.
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