Increasing computation demand of machine learning (ML) applications (recommender system, image classification, speech recognition, and so on) calls for the development of specialized hardware for ML and neuromorphic computing. New memories, such as resistive random access memory (RRAM), can be used to store weights of neural networks and to accelerate matrix multiplication, the dominant operation in neural networks. One of the key challenges for RRAM-based neural networks is to achieve bidirectional analog conductance modulation for online training. This article provides a programming scheme (SRA: small RESET voltage amplitude and appropriate SET voltage) to achieve bidirectional analog conductance modulation of RRAM devices. We find that both abrupt and gradual SET can be obtained for the same device. The controlling parameters for modulating the gradual SET behavior are the SET voltage and the local device temperature. We suggest that the filament morphology before SET may be the key to understanding this phenomenon; gradual SET is obtained when the filaments have a single-layer gap in the RESET state, and abrupt SET is obtained when the filaments have a multilayer gap in the RESET state.

中文翻译：

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基于 RRAM 的神经网络的双向模拟电导调制


机器学习 (ML) 应用（推荐系统、图像分类、语音识别等）不断增长的计算需求需要开发用于 ML 和神经形态计算的专用硬件。新存储器，例如电阻式随机存取存储器（RRAM），可用于存储神经网络的权重并加速矩阵乘法（神经网络中的主要运算）。基于 RRAM 的神经网络的关键挑战之一是实现用于在线训练的双向模拟电导调制。本文提供了一种编程方案（SRA：小的RESET电压幅度和适当的SET电压）来实现RRAM器件的双向模拟电导调制。我们发现同一设备可以同时获得突然和渐进的 SET。用于调节逐渐设置行为的控制参数是设置电压和本地器件温度。我们认为SET前的丝状形态可能是理解这一现象的关键；当灯丝在RESET状态下具有单层间隙时获得逐渐SET，而当灯丝在RESET状态下具有多层间隙时获得突然SET。