Electronics and Telecommunications Trends (전자통신동향분석)

Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Trend of AI Neuromorphic Semiconductor Technology

인공지능 뉴로모픽 반도체 기술 동향

  • 오광일 (초경량지능형반도체연구실) ;
  • 김성은 (초경량지능형반도체연구실) ;
  • 배영환 (초경량지능형반도체연구실) ;
  • 박경환 (초경량지능형반도체연구실) ;
  • 권영수 (지능형반도체연구본부)
  • Published : 2020.06.01
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Abstract

Neuromorphic hardware refers to brain-inspired computers or components that model an artificial neural network comprising densely connected parallel neurons and synapses. The major element in the widespread deployment of neural networks in embedded devices are efficient architecture for neuromorphic hardware with regard to performance, power consumption, and chip area. Spiking neural networks (SiNNs) are brain-inspired in which the communication among neurons is modeled in the form of spikes. Owing to brainlike operating modes, SNNs can be power efficient. However, issues still exist with research and actual application of SNNs. In this issue, we focus on the technology development cases and market trends of two typical tracks, which are listed above, from the point of view of artificial intelligence neuromorphic circuits and subsequently describe their future development prospects.

Keywords

Acknowledgement

This work was supported by the ICT R&D program of MSIT/IITP[2018-0-00197, Development of ultra-low power intelligent edge SoC technology based on lightweight RISC-V processor].

References

  1. https://www.qualcomm.com/news/onq/2013/10/10/introducing-qualcomm-zeroth-processors-brain-inspiredcomputing
  2. https://www.qualcomm.com/products/snapdragon-865-5gmobile-platform
  3. https://www.samsung.com/semiconductor/minisite/exynos/products/mobileprocessor/exynos-980/
  4. https://www.macworld.com/article/3442716/inside-applesa13-bionic-system-on-chip.html
  5. https://consumer.huawei.com/en/campaign/kirin-990-series/
  6. B. V. Benjamin et al., "Neurogrid: A mixed-analog-digital multichip system for large-scale neural simulations," Proc. IEEE, vol. 102, no. 5, May. 2014, pp. 699-716. https://doi.org/10.1109/JPROC.2014.2313565
  7. E. Painkras et al., "SpiNNaker: A 1 W 18 core system-onchip for massively-parallel neural network simulation," IEEE J. Solid-State Circuits, vol. 48, no. 8, Aug. 2013, pp. 1943-1953. https://doi.org/10.1109/JSSC.2013.2259038
  8. F. Akopyan et al.,"TrueNorth: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Neurosynaptic Chip," IEEE Trans. Comput.-Aided Desgin Integr. Circuits Syst., vol. 34, no. 10, Oct. 2015, pp. 1537-1557. https://doi.org/10.1109/TCAD.2015.2474396
  9. L. Lapicque, "Recherches Quantitatives sur L'excitation E'lectrique des Nerfs Traite'e Comme une Polarization," J. Physiol. Pathol. Gen., vol.9, 1907, pp. 620-635.
  10. J. Schemmel et al., "Live demonstration: A scaled-down version of the BrainScaleS wafer-scale neuromorphic system," in Proc. IEEE Int. Symp. Circuits Syst. (ISCAS), Seoul, Rep. of Kroea, May 2012, doi: 10.1109/ISCAS.2012.6272131
  11. S. Moradi et al., "A Scalable Multicore Architecture with Heterogeneous Memory Structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)," IEEE Trans. Biomed. Circuits Syst., vol. 12, no. 1, Feb. 2018, pp. 106-122. https://doi.org/10.1109/tbcas.2017.2759700
  12. M. Davies et al., "Loihi: A Neuromorphic Manycore Processor with On-Chip Learning," IEEE Micro, vol. 38, no. 1, Jan. 2018, pp. 82-99. https://doi.org/10.1109/mm.2018.112130359
  13. https://en.wikipedia.org/wiki/File:NeuroGridBoard.jpeg
  14. https://www.flickr.com/photos/i bm_ research_zurich/26101819225
  15. https://commons.wikimedia.org /wiki/File:Core_Top-Level_Microarchitecture.png