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Computational Investigation of the Potential and Limitations of Machine Learning with Neural Network Circuits Based on Synaptic Transistors.
Manzhos, Sergei; Chen, Qun Gao; Lee, Wen-Ya; Heejoo, Yoon; Ihara, Manabu; Chueh, Chu-Chen.
Afiliación
  • Manzhos S; School of Materials and Chemical Technology, Tokyo Institute of Technology, Ookayama 2-12-1, Meguro-ku, Tokyo 152-8552, Japan.
  • Chen QG; Department of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taipei 106, Taiwan.
  • Lee WY; Department of Chemical Engineering and Biotechnology, National Taipei University of Technology, Taipei 106, Taiwan.
  • Heejoo Y; School of Materials and Chemical Technology, Tokyo Institute of Technology, Ookayama 2-12-1, Meguro-ku, Tokyo 152-8552, Japan.
  • Ihara M; School of Materials and Chemical Technology, Tokyo Institute of Technology, Ookayama 2-12-1, Meguro-ku, Tokyo 152-8552, Japan.
  • Chueh CC; Department of Chemical Engineering, National Taiwan University, Taipei 10617, Taiwan.
J Phys Chem Lett ; 15(27): 6974-6985, 2024 Jul 11.
Article en En | MEDLINE | ID: mdl-38941557
ABSTRACT
Synaptic transistors have been proposed to implement neuron activation functions of neural networks (NNs). While promising to enable compact, fast, inexpensive, and energy-efficient dedicated NN circuits, they also have limitations compared to digital NNs (realized as codes for digital processors), including shape choices of the activation function using particular types of transistor implementation, and instabilities due to noise and other factors present in analog circuits. We present a computational study of the effects of these factors on NN performance and find that, while accuracy competitive with traditional NNs can be realized for many applications, there is high sensitivity to the instability in the shape of the activation function, suggesting that, when highly accurate NNs are required, high-precision circuitry should be developed beyond what has been reported for synaptic transistors to date.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Phys Chem Lett Año: 2024 Tipo del documento: Article País de afiliación: Japón 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: J Phys Chem Lett Año: 2024 Tipo del documento: Article País de afiliación: Japón Pais de publicación: Estados Unidos