基于$α$ - IGZO纳米片和双层电阻存储器的超低功耗3D嵌入式卷积神经网络立方体
Ultra Low Power 3D-Embedded Convolutional Neural Network Cube Based on $α$-IGZO Nanosheet and Bi-Layer Resistive Memory
摘要：
在本文中，我们提出和评估的3D嵌入式神经形态计算块的性能的基础上，铟镓锌氧化物（$α$ -IGZO）的纳米片晶体管和双层电阻存储器件。我们制作了具有Ta $_2$ O $_5$和Al $_2$ O $_3$层的双层阻变随机存取存储器（RRAM）器件。已对器械进行了表征和建模。RRAM和基于$α$ -IGZO的嵌入式纳米片结构的紧凑模型已被用于评估具有用于神经形态应用的RRAM的8个垂直堆叠的基于$α$ -IGZO的纳米片层的系统级性能。该模型考虑了具有均匀位线（BL）、选择线（SL）和字线（WL）电阻的设计空间。最后，我们用我们提出的8层堆叠的基于纳米片的嵌入式存储器模拟了加权求和操作，并评估了VGG-16卷积神经网络（CNN）用于Fashion-MNIST和CIFAR-10数据识别的性能，其在器件变化中分别产生了92%和75%的准确度。
英文摘要：
In this paper we propose and evaluate the performance of a 3D-embedded neuromorphic computation block based on indium gallium zinc oxide ($α$-IGZO) based nanosheet transistor and bi-layer resistive memory devices. We have fabricated bi-layer resistive random-access memory (RRAM) devices with Ta$_2$O$_5$ and Al$_2$O$_3$ layers. The device has been characterized and modeled. The compact models of RRAM and $α$-IGZO based embedded nanosheet structures have been used to evaluate the system-level performance of 8 vertically stacked $α$-IGZO based nanosheet layers with RRAM for neuromorphic applications. The model considers the design space with uniform bit line (BL), select line (SL), and word line (WL) resistance. Finally, we have simulated the weighted sum operation with our proposed 8-layer stacked nanosheet-based embedded memory and evaluated the performance for VGG-16 convolutional neural network (CNN) for Fashion-MNIST and CIFAR-10 data recognition, which yielded 92% and 75% accuracy respectively with drop out layers amid device variation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
