Neuromorphic Technology Based on Charge Storage Memory Devices
| Autor: | Chul-Heung Kim, Soochang Lee, Jong-Ho Lee, Dong Hwan Lee, Suhwan Lim, Nagyong Choi, Jong-Ho Bae, Byung-Gook Park, Tackhwi Lee, S.-C. Chung, Sung-Tae Lee |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
010302 applied physics
Spiking neural network Hardware_MEMORYSTRUCTURES Computer science 020208 electrical & electronic engineering NAND gate 02 engineering and technology Memristor 01 natural sciences Flash memory law.invention Neuromorphic engineering law Logic gate 0103 physical sciences Learning rule 0202 electrical engineering electronic engineering information engineering Electronic engineering Unsupervised learning Hardware_LOGICDESIGN |
| Zdroj: | 2018 IEEE Symposium on VLSI Technology. |
| DOI: | 10.1109/vlsit.2018.8510667 |
| Popis: | Four synaptic devices are introduced for spiking neural networks (SNNs) and deep neural networks (DNNs). Unsupervised learning is successfully demonstrated by applying the STDP learning rule reflecting the LTP/LTD characteristics of the fabricated TFT-type NOR flash memory cells. Gated Schottky diode (GSD) and vertical NAND flash cell are proposed as synaptic device for DNNs. Using matched simulation, we obtained higher learning accuracy with GSD and NAND synaptic devices compared to that with a memristor-based synapse. Measured synaptic properties of the vertical NAND cells are reported for the first time. |
| Databáze: | OpenAIRE |
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