Excellent Pattern Recognition Accuracy of Neural Networks Using Hybrid Synapses and Complementary Training

To overcome the performance degradation in hardware neural networks (NNs) with non-ideal synapse devices, we proposed a novel neuromorphic architecture with both TiO x -based interfacial RRAM and CBRAM-based filamentary RRAM for highly accurate NN training and long-term inference reliability. We use...

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Veröffentlicht in:	IEEE electron device letters 2021-04, Vol.42 (4), p.609-612
Hauptverfasser:	Kwak, Myonghoon, Choi, Wooseok, Heo, Seongjae, Lee, Chuljun, Nikam, Revannath, Kim, Seyoung, Hwang, Hyunsang
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Artificial neural networks Crossbar array Hardware Inference Linearity Neural networks Object recognition Pattern recognition Performance degradation Performance evaluation synapse device Synapses Titanium oxides Training
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