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Physical Reservoir Computing Using van der Waals Ferroelectrics for Acoustic Keyword Spotting.
Cao, Yi; Zhang, Zefeng; Qin, Bo-Wei; Sang, Weihui; Li, Honghong; Wang, Tinghao; Tan, Feixia; Gan, Yang; Zhang, Xumeng; Liu, Tao; Xiang, Du; Lin, Wei; Liu, Qi.
Afiliación
  • Cao Y; State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China.
  • Zhang Z; School of Microelectronics, Fudan University, Shanghai 200433, China.
  • Qin BW; State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China.
  • Sang W; Research Institute of Intelligent Complex Systems and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China.
  • Li H; Research Institute of Intelligent Complex Systems and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China.
  • Wang T; Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China.
  • Tan F; Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception, Institute of Optoelectronics and Department of Materials Science, Fudan University, Shanghai 200433, China.
  • Gan Y; State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China.
  • Zhang X; School of Microelectronics, Fudan University, Shanghai 200433, China.
  • Liu T; State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China.
  • Xiang D; School of Microelectronics, Fudan University, Shanghai 200433, China.
  • Lin W; State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China.
  • Liu Q; School of Microelectronics, Fudan University, Shanghai 200433, China.
ACS Nano ; 18(34): 23265-23276, 2024 Aug 27.
Article en En | MEDLINE | ID: mdl-39140427
ABSTRACT
Acoustic keyword spotting (KWS) plays a pivotal role in the voice-activated systems of artificial intelligence (AI), allowing for hands-free interactions between humans and smart devices through information retrieval of the voice commands. The cloud computing technology integrated with the artificial neural networks has been employed to execute the KWS tasks, which however suffers from propagation delay and the risk of privacy breach. Here, we report a single-node reservoir computing (RC) system based on the CuInP2S6 (CIPS)/graphene heterostructure planar device for implementing the KWS task with low computation cost. Through deliberately tuning the Schottky barrier height at the ferroelectric CIPS interfaces for the thermionic injection and transport of the electrons, the typical nonlinear current response and fading memory characteristics are achieved in the device. Additionally, the device exhibits diverse synaptic plasticity with an excellent separation capability of the temporal information. We construct a RC system through employing the ferroelectric device as the physical node to spot the acoustic keywords, i.e., the natural numbers from 1 to 9 based on simulation, in which the system demonstrates outstanding performance with high accuracy rate (>94.6%) and recall rate (>92.0%). Our work promises physical RC in single-node configuration as a prospective computing platform to process the acoustic keywords, promoting its applications in the artificial auditory system at the edge.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: ACS Nano Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: ACS Nano Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos