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 32×2 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.

Multi-ReRAM synapses for artificial neural network training / Boybat, I.; Giovinazzo, C.; Shahrabi, E.; Krawczuk, I.; Giannopoulos, I.; Piveteau, C.; Le Gallo, M.; Ricciardi, C.; Sebastian, A.; Eleftheriou, E.; Leblebici, Y.. - ELETTRONICO. - 2019-:(2019), pp. 1-5. ((Intervento presentato al convegno 2019 IEEE International Symposium on Circuits and Systems (ISCAS) [10.1109/ISCAS.2019.8702714].

Multi-ReRAM synapses for artificial neural network training

Giovinazzo C.;Ricciardi C.;
2019

Abstract

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 32×2 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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2809077