Investigation of Read Disturb and Bipolar Read Scheme on Multilevel RRAM-Based Deep Learning Inference Engine
Investigation of Read Disturb and Bipolar Read Scheme on Multilevel RRAM-Based Deep Learning Inference Engine
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DOI：
10.1109/ted.2020.2985013
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发表时间：
2020-06-01
影响因子：
3.1
通讯作者：
Yu, Shimeng
中科院分区：
文献类型：
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作者：
Shim, Wonbo;Luo, Yandong;Yu, Shimeng
基于多级阻变随机存取存储器（RRAM）的突触阵列能够实现向量 - 矩阵乘法的并行计算，以加速机器学习推理；然而，由于模拟电流沿列累加，单元的任何电导漂移都可能导致推理精度下降。在本文中，在基于2位HfO₂ RRAM阵列的测试器件上对读干扰导致的电导漂移特性进行了统计测量。通过垂直和横向细丝生长机制对四种状态的漂移行为进行了经验建模。此外，提出并测试了一种双极读取方案，以增强对读干扰的抗性。将建模的读干扰和提出的补偿方案纳入一个类似VGG的卷积神经网络中，用于CIFAR - 10数据集的推理。
The multilevel resistive random access memory (RRAM)-based synaptic array can enable parallel computations of vector-matrix multiplication for machine learning inference acceleration; however, any conductance drift of the cell may induce an inference accuracy drop because the analog current is summed up along the column. In this article, the read disturb-induced conductance drift characteristic is statistically measured on a test vehicle based on 2-bit HfO2 RRAM array. The drift behavior of four states is empirically modeled by a vertical and lateral filament growth mechanism. Furthermore, a bipolar read scheme is proposed and tested to enhance the resilience against the read disturb. The modeled read disturb and proposed compensation scheme are incorporated into a VGG-like convolutional neural network for CIFAR-10 data set inference.