Web of Science: 2 cites, Scopus: 2 cites, Google Scholar: cites
Ternary Neural Networks Based on on/off Memristors : set-Up and Training
Morell Pérez, Antoni (Universitat Autònoma de Barcelona. Departament de Telecomunicació i Enginyeria de Sistemes)
Díaz Machado, Elvis (Universitat Autònoma de Barcelona. Departament de Telecomunicació i Enginyeria de Sistemes)
Miranda, Enrique (Universitat Autònoma de Barcelona. Departament d'Enginyeria Electrònica)
Boquet, Guillem (Universitat Oberta de Catalunya)
López Vicario, José (Universitat Autònoma de Barcelona. Departament de Telecomunicació i Enginyeria de Sistemes)

Data: 2022
Resum: Neuromorphic systems based on hardware neural networks (HNNs) are expected to be an energy and time-efficient computing architecture for solving complex tasks. In this paper, we consider the implementation of deep neural networks (DNNs) using crossbar arrays of memristors. More specifically, we considered the case where such devices can be configured in just two states: the low-resistance state (LRS) and the high-resistance state (HRS). HNNs suffer from several non-idealities that need to be solved when mapping our software-based models. A clear example in memristor-based neural networks is conductance variability, which is inherent to resistive switching devices, so achieving good performance in an HNN largely depends on the development of reliable weight storage or, alternatively, mitigation techniques against weight uncertainty. In this manuscript, we provide guidelines for a system-level designer where we take into account several issues related to the set-up of the HNN, such as what the appropriate conductance value in the LRS is or the adaptive conversion of current outputs at one stage to input voltages for the next stage. A second contribution is the training of the system, which is performed via offline learning, and considering the hardware imperfections, which in this case are conductance fluctuations. Finally, the resulting inference system is tested in two well-known databases from MNIST, showing that is competitive in terms of classification performance against the software-based counterpart. Additional advice and insights on system tuning and expected performance are given throughout the paper.
Ajuts: Agencia Estatal de Investigación TEC2017-84321-C4-4-R
Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/SGR-1670
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. Creative Commons
Llengua: Anglès
Document: Article ; Versió publicada
Matèria: Hardware neural networks ; Ternary networks ; On/off memristors
Publicat a: Electronics, Vol. 11, Issue 10 (May 2022) , art. 1526, ISSN 2079-9292

DOI: 10.3390/electronics11101526


17 p, 1.6 MB

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