전자통신동향분석 (Electronics and Telecommunications Trends)

한국전자통신연구원 (Electronics and Telecommunications Research Institute)
권호 표지
DOI QR코드

DOI QR Code

뉴로모픽 포토닉스 기술 동향

Trends in Neuromorphic Photonics Technology

  • 발행 : 2020.08.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

The existing Von Neumann architecture places limits to data processing in AI, a booming technology. To address this issue, research is being conducted on computing architectures and artificial neural networks that simulate neurons and synapses, which are the hardware of the human brain. With high-speed, high-throughput data communication infrastructures, photonic solutions today are a mature industrial reality. In particular, due to the recent outstanding achievements of artificial neural networks, there is considerable interest in improving their speed and energy efficiency by exploiting photonic-based neuromorphic hardware instead of electronic-based hardware. This paper covers recent photonic neuromorphic studies and a classification of existing solutions (categorized into multilayer perceptrons, convolutional neural networks, spiking neural networks, and reservoir computing).

키워드

참고문헌

  1. L. D. Marinis et al., "Photonic Neural Networks: A Survey," IEEE Access, vol. 7, 2019, pp. 175827-175841. https://doi.org/10.1109/ACCESS.2019.2957245
  2. M. A. Zidan et al., "The future of electronics based on memristive systems," Nature Electron. vol. 1, 2018, pp. 22-29. https://doi.org/10.1038/s41928-017-0006-8
  3. 문승언, "차세대 뉴로모픽 하드웨어 기술동향," 전자통신동향분석, 제33권 제6호, 2018, pp. 58-68. https://doi.org/10.22648/ETRI.2018.J.330607
  4. B. J. Shastri et al., "Principles of Neuromorphic Photonics," arXiv: 1801.00016, 2018.
  5. Y. Shen et al., "Deep learning with coherent nanophotonic circuits," Nature Photon., vol. 11, 2017, pp. 441-447. https://doi.org/10.1038/nphoton.2017.93
  6. A. Mehrabian et al., "PCNNA: A Photonic Convolutional Neural Network Accelerator," arXiv:1807.08792, 2018.
  7. M. A. Nahmias et al., "A TeraMAC neuromorphic photonic processor," in Proc. IEEE Photon. Conf. (IPC), 2018, pp. 1-2.
  8. C. Rios et al., "Integrated all-photonic non-volatile multi-level memory," Nature Photon., vol. 9, 2015, pp. 725-733. https://doi.org/10.1038/nphoton.2015.182
  9. I. Chakraborty et al., "Toward Fast Neural Computing using All-Photonic Phase Change Spiking Neurons," Scientific Reports, vol. 8, 2018, pp. 1-9. https://doi.org/10.1038/s41598-017-17765-5
  10. J. Feldmann et al., "All-optical spiking neurosynaptic networks with self-learning capabilities," Nature, vol. 569, 2019, pp. 208-215. https://doi.org/10.1038/s41586-019-1157-8
  11. A. Katumba et al., "Neuromorphic Computing Based on Silicon Photonics and Reservoir Computing," IEEE J, Select. Topics Quantum Electron., vol. 24, 2018, p. 8300310.