https://scholars.lib.ntu.edu.tw/handle/123456789/581288
標題: | Robust Brain-Inspired Computing: On the Reliability of Spiking Neural Network Using Emerging Non-Volatile Synapses | 作者: | Wei M.-L Amrouch H Sung C.-L Lue H.-T Yang C.-L Wang K.-C Lu C.-Y. CHIA-LIN YANG |
關鍵字: | Low power electronics; Nonvolatile storage; Reliability; Brain-inspired computing; Classification accuracy; Intelligent applications; Low-power consumption; Membrane capacitors; Non-volatile memory; Spiking neural network(SNN); Spiking neural networks; Neural networks | 公開日期: | 2021 | 卷: | 2021-March | 來源出版物: | IEEE International Reliability Physics Symposium Proceedings | 摘要: | A spiking neural network (SNN) with non-volatile memory synapses can facilitate an ideal analog approach with low power consumption for intelligent applications. However, reliability issues would arise due to the characteristics of Non-Volatile Memory synapses operating under the limited size of a neural circuit component called membrane capacitor, resulting in low precision MAC. Through a simulation study, we identified several important criteria of the memory synapses that affect performance of the SNN for the membrane capacitor size of 1pF. (1) The required ON-OFF ratio of synapse needs to be >1000 to preserve classification accuracy. (2) Low on-current Ion (<10uA) is preferred for low power. (3) The variation and error of Ion should be lower than +/-10% of mean value. ? 2021 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105599032&doi=10.1109%2fIRPS46558.2021.9405141&partnerID=40&md5=51c329cc197e721fd3be551eb07a5cb7 https://scholars.lib.ntu.edu.tw/handle/123456789/581288 |
ISSN: | 15417026 | DOI: | 10.1109/IRPS46558.2021.9405141 |
顯示於: | 資訊工程學系 |
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