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.
2015
6A.1.1
6A.1.6
REVIEWED
Event name | Event place | Event date |
Monterey, CA, USA | 19-23 April 2015 | |