Journal
SCIENTIFIC REPORTS
Volume 8, Issue -, Pages -Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/s41598-018-21057-x
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Funding
- Nano.Material Technology Development Program through the National Research Foundation (NRF) of Korea - Ministry of Science, ICT and Future Planning [2016M3A7B4910430]
- Basic Science Research Program through the National Research Foundation (NRF) of Korea - Ministry of Science, ICT and Future Planning [2016R1D1A1B03930162, 2016R1A2B4011366, 2016R1A5A1012966]
- Future Semiconductor Device Technology Development Program - MOTIE (Ministry of Trade, Industry Energy) [10067739]
- KSRC (Korea Semiconductor Research Consortium)
- National Research Foundation of Korea [2016R1D1A1B03930162, 2016M3A7B4910430] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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Neuromorphic systems (hardware neural networks) derive inspiration from biological neural systems and are expected to be a computing breakthrough beyond conventional von Neumann architecture. Interestingly, in neuromorphic systems, the processing and storing of information can be performed simultaneously by modulating the connection strength of a synaptic device (i.e., synaptic weight). Previously investigated synaptic devices can emulate the functionality of biological synapses successfully by utilizing various nano-electronic phenomena; however, the impact of intrinsic synaptic device variability on the system performance has not yet been studied. Here, we perform a device-to-system level simulation of different synaptic device variation parameters in a designed neuromorphic system that has the potential for unsupervised learning and pattern recognition. The effects of variations in parameters such as the weight modulation nonlinearity (NL), the minimum-maximum weight (G(min) and G(max)), and the weight update margin (Delta G) on the pattern recognition accuracy are analyzed quantitatively. These simulation results can provide guidelines for the continued design and optimization of a synaptic device for realizing a functional large-scale neuromorphic computing system.
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