Organic memristive devices for perceptron applications
Organic memristive devices for perceptron applications
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DOI：
10.1088/1361-6463/aac98f
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发表时间：
2018-07-18
影响因子：
3.4
通讯作者：
Iannotta, S.
中科院分区：
文献类型：
--
作者：
Battistoni, S.;Erokhin, V.;Iannotta, S.
在人工智能领域，神经形态学应用中最具挑战性的任务之一是能够在信息处理过程中学习的人工神经网络的硬件实现（模式识别和分类、近似、预测等）。在这种情况下，由于它们能够保持对它们先前导电状态的记忆，忆阻器被广泛认为是有效实现ANN的有希望的元件。在本文中，我们提出了一个简短的审查的设计和硬件实现的单层和双层人工神经网络，能够执行线性可分离和不可分离的逻辑分类，使用有机忆阻器件作为元素，确保网络的权重调整。
One of the most challenging tasks in neuromorphic applications, in the field of artificial intelligence, is the hardware realization of artificial neural networks (ANNs) which are able to learn during information processing (pattern recognition and classification, approximation, prediction, etc).In this scenario, thanks to their ability to keep the memory of their previous conductive state, memristors are widely considered as promising elements for the efficient implementation of ANNs. In this paper we present a short review of the design and the hardware realization of single and double layer ANNs, which are able to perform linearly separable and non-separable logic classification, using organic memristive devices as elements, ensuring the network weight adjustment.