We present a new electronic synapse for neuromorphic computing consisting of a 1T1R structure based on HfO2RRAM technology, and capable of STDP and pattern learning. Power consumption is reduced by adopting short POST spike and burst-mode integration. MNIST classification shows promising learning and classification efficiency. These results support RRAM as an enabling technology for low-power neuromorphic hardware.

Novel RRAM-enabled 1T1R synapse capable of low-power STDP via burst-mode communication and real-Time unsupervised machine learning

Ambrogio, S.;Balatti, S.;Milo, V.;Carboni, R.;Wang, Z.;Ielmini, D.
2016-01-01

Abstract

We present a new electronic synapse for neuromorphic computing consisting of a 1T1R structure based on HfO2RRAM technology, and capable of STDP and pattern learning. Power consumption is reduced by adopting short POST spike and burst-mode integration. MNIST classification shows promising learning and classification efficiency. These results support RRAM as an enabling technology for low-power neuromorphic hardware.
Digest of Technical Papers - Symposium on VLSI Technology
9781509006373
Electrical and Electronic Engineering
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1035661
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