神经形态工程学
MNIST数据库
横杆开关
计算机科学
感知器
人工神经网络
可靠性(半导体)
编码(内存)
人工智能
一般化
计算机硬件
模式识别(心理学)
功率(物理)
数学
量子力学
电信
物理
数学分析
作者
R. M. Shelby,Geoffrey W. Burr,Irem Boybat,Carmelo di Nolfo
标识
DOI:10.1109/irps.2015.7112755
摘要
A large-scale artificial neural network, a three-layer perceptron, is implemented using two phase-change memory (PCM) devices to encode the weight of each of 164,885 synapses. The PCM conductances are programmed using a crossbar-compatible pulse scheme, and the network is trained to recognize a 5000-example subset of the MNIST handwritten digit database, achieving 82.2% accuracy during training and 82.9% generalization accuracy on unseen test examples. A simulation of the network performance is developed that incorporates a statistical model of the PCM response, allowing quantitative estimation of the tolerance of the network to device variation, defects, and conductance response.
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