用于图像分类卷积神经网络突触的电阻性RAM计算失效分析

Nagaraj Lakshmana Prabhu, N. Raghavan
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引用次数: 0

摘要

人们正在探索各种NVM技术来实现神经形态系统,包括电阻性RAM、铁电RAM、相变RAM、自旋传递扭矩RAM和NAND闪存。本文讨论了RRAM在此类应用中的潜力,并评估了神经网络图像分类背景下的关键性能和可靠性指标。作者得出结论,使用替代材料堆叠和多层电介质可以进一步改善精度-功率权衡,从而更好地控制控制RRAM开关特性的氧空位或金属丝化过程。
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Computational Failure Analysis of Resistive RAM Used as a Synapse in a Convolutional Neural Network for Image Classification
Various NVM technologies are being explored for neuromorphic system realization, including resistive RAM, ferroelectric RAM, phase change RAM, spin transfer torque RAM, and NAND flash. This article discusses the potential of RRAM for such applications and evaluates key performance and reliability metrics in the context of neural network image classification. The authors conclude that the accuracy-power tradeoff may be further improved using alternative material stacks and multi-layer dielectrics so as to achieve better control of the oxygen vacancy or metallic filamentation process that governs RRAM switching characteristics.
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