Non-volatile memory as hardware synapse in neuromorphic computing: A first look at reliability issues

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.


Published in:
2015 IEEE International Reliability Physics Symposium, 6A.1.1-6A.1.6
Presented at:
2015 IEEE International Reliability Physics Symposium (IRPS), Monterey, CA, USA, 19-23 April 2015
Year:
2015
Publisher:
IEEE
Laboratories:




 Record created 2017-06-28, last modified 2018-09-13


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