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物理水库计算使用范德瓦尔斯铁电器用于声学关键词发现

Yi Cao^1,2, Zefeng Zhang^1,3, Bo-Wei Qin^3,4

¹State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China.

|August 14, 2024

概括

这项研究介绍了一种新型的单节点储计算系统,使用CuInP2S6 / 石墨烯设备进行高效的声学关键词发现. 这种方法为基于云的人工智能系统提供了一个低成本,保护隐私的替代方案.

科学领域:

材料科学材料科学材料科学
人工智能的人工智能
神经形态计算是一种神经形态计算.

背景情况:

对语音激活的人工智能系统而言,声学关键词发现 (Acoustic keyword spotting, KWS) 是至关重要的,但基于云的解决方案面临隐私和延迟问题.
现有的KWS方法通常依赖于复杂的人工神经网络 (ANN) 和云计算,这给边缘设备带来了挑战.

研究的目的:

为了开发一个低计算成本,单节点储计算 (RC) 系统用于声学关键字发现.
探索CuInP2S6 (CIPS) /石墨烯异构装置在神经形态计算应用中的潜力.

主要方法:

使用CuInP2S6 (CIPS) /石墨烯异构结构制造一个平面装置.
在铁电 CIPS 接口上调整 Schottky 屏障高度,以实现非线性电流响应和色记忆.
实现一个单节点的RC系统,使用铁电装置作为声学关键字检测的物理节点.

主要成果:

CIPS/石墨烯装置显示了非线性电流响应和色的记忆特征.
该设备表现出多样化的突触可塑性和有效的时间信息分离.
该RC系统通过模拟实现了高精度 (>94.6%) 和回忆率 (>92.0%) 在识别声学关键字 (自然数字1-9).

结论:

使用铁电器件的单节点物理储计算是声学关键字处理的有希望的平台.
这种方法为边缘AI应用提供了潜在的解决方案,增强隐私并减少语音激活系统中的延迟.
开发的CIPS/石墨烯装置显示了未来人工听觉系统的巨大潜力.

关键词:

声学关键词发现声学关键词发现高精度和回忆率的高精度.非线性电流响应和消失的记忆.物理水库计算计算物理水库计算范德瓦尔斯铁电公司

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