论文标题
用二元RRAM阵列实现VMM的方法：使用oxram横杆进行二进制 - 亚甲的实验演示
Methodology for Realizing VMM with Binary RRAM Arrays: Experimental Demonstration of Binarized-ADALINE Using OxRAM Crossbar
论文作者
论文摘要
在本文中，我们提出了一种有效的硬件映射方法，用于在电阻内存（RRAM）阵列上实现向量矩阵乘法（VMM）。使用所提出的VMM计算技术，我们实验表明了oxram横杆上的二氧化剂 - 辅助（自适应线性）分类器。使用Ni/3-nm HFO2/7 nm al-Doped-Tio2/Tin设备堆栈的8x8 oxram横梁。在UCI癌症数据集上，对二元 - 亚非盐分类器进行了重量训练。体重后生成oxram阵列是使用定制测试台上的建议的重量映射技术仔细编程到二进制重量态的。我们的VMM动力二氧化醛网络在模拟中达到了78％的分类准确性，实验的分类精度为67％。发现实验精度主要是由于横杆固有的偷偷摸摸路径问题和RRAM设备编程的可变性而下降。
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.
