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Memristive based device arrays combined with Spike based coding can enable efficient implementations of embedded neuromorphic circuits

Abstract : Since the rapid development of post-CMOS technologies in the last decade, there has been a growing interest in utilizing them for implementing neuromorphic or brain-like computing machines. Besides attempts to build realistic circuits that would mimic the functioning of biological neurons as close as possible [1][2], our team is focused on implementing neuromorphic circuits suitable for embedded applications. This objective puts the emphasis on two majors concerns: integration and energy efficiency. In our quest for ultimate integration, we first report on investigating for the best synapse-like technology among the realm of potential candidates. We then report our investigations on the feasibility of large crossbars of synapse-like devices and show that there is still a long way ahead. Finally in an effort to tackle the energy problem, we introduce spike based coding for deep neuromorphic architectures and discuss our argument that spike coding combined with memristive synaptic devices could pave the way for future embedded neuromorphic circuits.
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https://hal-cea.archives-ouvertes.fr/cea-01839847
Contributor : Léna Le Roy <>
Submitted on : Monday, July 16, 2018 - 10:05:26 AM
Last modification on : Monday, February 10, 2020 - 6:14:16 PM

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C. Gamrat, O. Bichler, D. Roclin. Memristive based device arrays combined with Spike based coding can enable efficient implementations of embedded neuromorphic circuits. 2015 IEEE International Electron Devices Meeting (IEDM), Dec 2015, Washington, DC, United States. pp.4.5.1-4.5.7, ⟨10.1109/IEDM.2015.7409626⟩. ⟨cea-01839847⟩

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