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人工泄漏集成和火传感神经元用于传感器内计算神经形态感知在边缘

Yubin Yuan^1,2, Runyu Gao^1,2, Qiang Wu^1,2

¹School of Microelectronics, Xi'an Jiaotong University, Xi'an 710049, China.

|May 26, 2023

概括

使用memristor技术的新型人工尖端感觉神经元 (ASSN) 实现了节能的神经形态计算. 这种传感器内计算方法整合了传感和处理,以实现先进的感知能力.

科学领域:

材料科学材料科学材料科学
神经科学是一个神经科学.
计算机工程计算机工程

背景情况:

神经形态计算比传统架构提供了更高的能源效率和数据带宽.
在传感器计算需要无集成的受体和神经元边缘处理.
人工尖端感觉神经元 (ASSN) 是神经形态系统中功能融合的关键.

研究的目的:

开发一个有漏洞的整合和发射 (LIF) 人工尖端感觉神经元 (ASSN) 具有双重感应能力.
展示ASSN的高工艺兼容性和集成制造的潜力.
探索先进边缘感知的自我适应和横向调节机制.

主要方法:

使用NbOx记忆器和a-IGZO薄膜晶体管 (TFT) 通过喷射沉积制造ASSN.
描述ASSN的尖峰编码能力 (速率和时间到第一个尖峰).
将NO2气体和紫外线光敏感度集成到a-IGZO TFT中,用于双神经形态感知.

主要成果:

该ASSN证明了神经形态信息传输的有效尖峰编码.
a-IGZO TFT表现出双重敏感性,诱导对NO2气体的抑制反应和对紫外线的刺激反应.
实施的自我适应和横向调节电路显示出成功的自我调节和针对目标事件的增强输出.

结论:

开发的ASSN,其双重灵敏度和集成计算,代表了传感器内计算的重大进步.
该设备的制造过程与集成兼容,为可扩展的神经形态系统铺平了道路.
经过证明的自我适应和横向调节能力使复杂环境中的多场景感知成为可能.

关键词:

一个LIF神经元的神经元.边缘计算是一种边缘计算.在传感器内进行计算.侧向调节是一种侧向调节.神经形态感知是一种神经形态感知.自己适应的自我适应.

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