Silicon carbide nanowire-based multifunctional and efficient visual synaptic devices for wireless transmission and neural network computing
Source abstract
Abstract
随着大数据和物联网的快速发展, 基于传统冯·诺依曼架构的计 算机存在吞吐量有限和能源利用效率低下的问题. 基于忆阻器的人工 神经网络计算系统被认为是克服这一瓶颈的候选之一. 本研究成功研 制了基于碳化硅纳米线薄膜的光电忆阻器, 不仅可以很好地实现生物 视觉突触的基本功能, 还可以实现更为复杂的树突神经元和巴甫洛夫 学习等类脑学习特性. 在视觉功能的基础上, 实现了集成在SiC忆阻器 中的感知、存储和计算的功能. 更重要的是, 得益于SiC纳米线视觉突 触优异的计算能力构建的脉冲神经网络能够实现早期肺癌病灶的识 别. 该方法迭代次数少, 检测准确率可达90%以上, 在医疗领域具有良好 的应用前景. 本研究为神经形态计算技术中基于SiC的感知-存储-计算 一体化人工智能设备的开发和推广提供了一条有前景的途径.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced Memory and Neural Computing · CCD and CMOS Imaging Sensors · Transition Metal Oxide Nanomaterials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (67)
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