PROJECT TITLE :
Optimization of Conductance Change in Pr1–xCaxMnO3-Based Synaptic Devices for Neuromorphic Systems
The optimization of conductance change behavior in synaptic devices primarily based on analog resistive memory is studied for the utilization in neuromorphic systems. Resistive memory based on Pr1-xCaxMnO3 (PCMO) is applied to a neural network application (classification of Modified National Institute of Standards and Technology handwritten digits employing a multilayer perceptron trained with backpropagation) beneath a large choice of simulated conductance change behaviors. Linear and symmetric conductance changes (e.g., self-similar response throughout both increasing and decreasing device conductance) are shown to supply the highest classification accuracies. Further improvements will be obtained using nonidentical coaching pulses, at the value of requiring measurement of individual conductance during training. Such a system can be expected to realize, with our existing PCMO-based synaptic devices, a generalization accuracy on a previously-unseen test set of 90.fifty fivepercent. These results are promising for hardware demonstration of high neuromorphic accuracies using existing synaptic devices.
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