Efficient Learning And Crossbar Operations With Atomically-Thin 2-D Material Compound Synapses
JOURNAL OF APPLIED PHYSICS(2018)
摘要
Accurate and efficient synaptic weight programming and vector-matrix multiplication are demonstrated using compound synapses constructed with ultralow power binary memristive devices having oxidized atomically thin two-dimensional hexagonal boron nitride (BNOx) filament formation layers. Experimental data of the resistive-switching current-voltage characteristics of BNOx memristors are used to formulate variation-aware models that enable statistically analyzing the trade-off between efficiency and accuracy as a function of the synaptic resolution (i.e., levels of synaptic weight programming). Results are compared with commonly reported oxide-based memristors indicating orders of magnitude (i.e.,similar to 10(5)) improvements in power efficiency and similar to 2-5x improvements in accuracy. Published by AIP Publishing.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络