Bypassing the von Neumann Bottleneck via Direct 300-GHz RF Conversion of Optical Computation Results

Electrical computers are widely used; however, their large power consumption remains a major concern. Optical computers, offering high-speed operation with lower power consumption, are an attractive alternative. Nevertheless, the limitation caused by the communication speed between a processor and memory, known as the von Neumann bottleneck, remains unresolved. One way to address this bottleneck is to use wireless signal transmission via electro-magnetic waves, which can greatly enhance the system's flexibility and reduce dependency on high-speed buses where their placement is challenging.

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Publication Details

Published
2025-06-23
DOI
https://doi.org/10.1109/cleo/europe-eqec65582.2025.11111524
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
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Bypassing the von Neumann Bottleneck via Direct 300-GHz RF Conversion of Optical Computation Results

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Neural Networks and Reservoir Computing
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Bypassing the von Neumann Bottleneck via Direct 300-GHz RF Conversion of Optical Computation Results

Kazuya Satõ, S. Kawanishi, Takasumi Tanabe, Shun Fujii, Ryo Sugano, Junnosuke Kokubu, Ayaka Yomoda, M. Imamura, Hitomi Uemura
article en

Abstract

Electrical computers are widely used; however, their large power consumption remains a major concern. Optical computers, offering high-speed operation with lower power consumption, are an attractive alternative. Nevertheless, the limitation caused by the communication speed between a processor and memory, known as the von Neumann bottleneck, remains unresolved. One way to address this bottleneck is to use wireless signal transmission via electro-magnetic waves, which can greatly enhance the system's flexibility and reduce dependency on high-speed buses where their placement is challenging.

Keio University (JP)
Affordable and clean energy
Openalex Percentile: Top 89%
Neural Networks and Reservoir Computing
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