Multi-ReRAM synapses for artificial neural network training

Metal-oxide-based resistive memory devices (ReRAM) are being actively researched as synaptic elements of neuromorphic co-processors for training deep neural networks (DNNs). However, device-level non-idealities are posing significant challenges. In this work we present a multi-ReRAM-based synaptic architecture with a counter-based arbitration scheme that shows significant promise. We present a 32x2 crossbar array comprising Pt/HfO2/Ti/TiN-based ReRAM devices with multi-level storage capability and bidirectional conductance response. We study the device characteristics in detail and model the conductance response. We show through simulations that an in-situ trained DNN with a multi-ReRAM synaptic architecture can perform handwritten digit classification task with high accuracies, only 2% lower than software simulations using floating point precision, despite the stochasticity, nonlinearity and large conductance change granularity associated with the devices. Moreover, we show that a network can achieve accuracies > 80% even with just binary ReRAM devices with this architecture.

Published in:
2019 Ieee International Symposium On Circuits And Systems (Iscas)
Presented at:
IEEE International Symposium on Circuits and Systems (IEEE ISCAS), Sapporo, JAPAN, May 26-29, 2019
Jan 01 2019
New York, IEEE

 Record created 2019-09-19, last modified 2019-12-05

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