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Pre-sensor computing with compact multilayer optical neural network.
Huang, Zheng; Shi, Wanxin; Wu, Shukai; Wang, Yaode; Yang, Sigang; Chen, Hongwei.
Afiliación
  • Huang Z; Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.
  • Shi W; Beijing National Research Center for Information Science and Technology, Beijing 100084, China.
  • Wu S; Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.
  • Wang Y; Beijing National Research Center for Information Science and Technology, Beijing 100084, China.
  • Yang S; Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.
  • Chen H; Beijing National Research Center for Information Science and Technology, Beijing 100084, China.
Sci Adv ; 10(30): eado8516, 2024 Jul 26.
Article en En | MEDLINE | ID: mdl-39058775
ABSTRACT
Moving computation units closer to sensors is becoming a promising approach to addressing bottlenecks in computing speed, power consumption, and data storage. Pre-sensor computing with optical neural networks (ONNs) allows extensive processing. However, the lack of nonlinear activation and dependence on laser input limits the computational capacity, practicality, and scalability. A compact and passive multilayer ONN (MONN) is proposed, which has two convolution layers and an inserted nonlinear layer, performing pre-sensor computations with designed passive masks and a quantum dot film for incoherent light. MONN has an optical length as short as 5 millimeters, two orders of magnitude smaller than state-of-the-art lens-based ONNs. MONN outperforms linear single-layer ONN across various vision tasks, off-loading up to 95% of computationally expensive operations into optics from electronics. Motivated by MONN, a paradigm is emerging for mobile vision, fulfilling the demands for practicality, miniaturization, and low power consumption.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sci Adv 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
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sci Adv Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos