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EnglishMetal-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).
| Titolo: | Multi-ReRAM synapses for artificial neural network training |
| Autori: | |
| Data di pubblicazione: | 2019 |
| Abstract: | Metal-oxide-based resistive memory devices (ReRAM) are being actively researched as synaptic elem...ents 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. |
| ISBN: | 978-1-7281-0397-6 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| 2019_IBM_IEEE.pdf | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
http://hdl.handle.net/11583/2809077
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