The Journal of the Convergence on Culture Technology (문화기술의 융합)

The International Promotion Agency of Culture Technology (국제문화기술진흥원)
권호 표지
DOI QR코드

DOI QR Code

A Structure of Spiking Neural Networks(SNN) Compiler and a performance analysis of mapping algorithm

Spiking Neural Networks(SNN)를 위한 컴파일러 구조와 매핑 알고리즘 성능 분석

  • Kim, Yongjoo (Artificial Intelligence Research Laboratory, Electronics and Telecommunications Research Institute (ETRI)) ;
  • Kim, Taeho
  • 김용주 (한국전자통신연구원 인공지능연구소) ;
  • 김태호 (한국전자통신연구원 인공지능연구소)
  • Received : 2022.07.28
  • Accepted : 2022.09.03
  • Published : 2022.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Research on artificial intelligence based on SNN (Spiking Neural Networks) is drawing attention as a next-generation artificial intelligence that can overcome the limitations of artificial intelligence based on DNN (Deep Neural Networks) that is currently popular. In this paper, we describe the structure of the SNN compiler, a system SW that generate code from SNN description for neuromorphic computing systems. We also introduce the algorithms used for compiler implementation and present experimental results on how the execution time varies in neuromorphic computing systems depending on the the mapping algorithm. The mapping algorithm proposed in the text showed a performance improvement of up to 3.96 times over a random mapping. The results of this study will allow SNNs to be applied in various neuromorphic hardware.

SNN(Spiking Neural Networks) 기반의 인공지능 연구는 현재 유행하는 DNN(Deep Neural Networks) 기반의 인공지능의 한계를 극복할 수 있는 차세대 인공지능으로서 주목받고 있다. 본 논문에서는 SNN 형태의 입력을 뉴로모픽 컴퓨팅 시스템에서 구동시킬 수 있는 시스템 SW인 SNN 컴파일러의 구조에 대하여 설명한다. 또한 컴파일러 구현을 위하여 사용된 알고리즘을 소개하고 매핑 알고리즘의 동작 형태에 따라 뉴로모픽 컴퓨팅 시스템에서 수행시간이 어떻게 달라지는지에 대한 실험결과를 제시한다. 본문에서 제안한 매핑 알고리즘은 랜덤 매핑에 비해 최대 3.96배의 수행속도 향상이 있었다. 해당 연구 결과를 통해 SNN들을 다양한 뉴로모픽 하드웨어에서 적용할 수 있을 것이다.

Keywords

Acknowledgement

이 논문은 2022년도 정부(과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원을 받아 수행된 연구임. (No.2018-0-00769, 인공지능 시스템을 위한 뉴로모픽 컴퓨팅 SW 플랫폼 기술 개발)

References

  1. G. E. Hinton, "Learning multiple layers of representation". Trends in Cognitive Sciences. 11(10): 428-4 https://doi.org/10.1016/j.tics.2007.09.004
  2. G. E. Hinton et al. "A Fast Learning Algorithmfor Deep Belief Nets" Neural Computation. 18(7): 1527-1554 https://doi.org/10.1162/neco.2006.18.7.1527
  3. D. Silver et al.. "Mastering the game of Go with deep neural networks and tree search". Nature. 529 (7587): 484-489. Bibcode:2016Natur.529..484S. doi:10.1038/nature16961. ISSN 0028-0836.
  4. A. Krizhevsky et al. "ImageNet Classification with Deep Convolutional Neural Networks" Communications of the ACM Vol. 60, No. 6
  5. M. Davies, et al., "Loihi: A Neuromorphic Manycore Processor with On-Chip Learning," IEEE Micro, vol. 38, no. 1, pp. 82-99, 2018. https://doi.org/10.1109/mm.2018.112130359
  6. F. Akopyan, J. Sawada, A. Cassidy, R. AlvarezIcaza, J. Arthur, P. Merolla et al. (2015). Truenorth: Design and tool flow of a 65 mw 1 million neuron programmable neurosynaptic chip. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 3 , 1537-1557
  7. B. F. Rubin "Qualcomm's Zeroth platform could make your smartphone much smarter". CNET. Retrieved March 10, 2015.
  8. F. Steve, B. Petrut, SpiNNaker : A spiking neural network architecture. Boston-Delpht : Now Publishers Inc, 2020. p.350 Computational Neuroscience, Aug. 2015.
  9. C. Pehle et al. The BrainScaleS-2 Accelerated Neuromorphic System With Hybrid Plasticity. Front Neurosci. 2022 Feb 24;16:795876. doi: 10.3389/fnins.2022.795876. PMID: 35281488; PMCID: PMC8907969
  10. A. Balaji, Y. Wu, A. Das, F. Catthoor, and S. Schaafsma, "Exploration of segmented bus as scalable global interconnect for neuromorphic computing," in Great Lakes Symposium on VLSI. ACM, 2019.
  11. K. Boahen "Neurogrid: Emulating a Million Neurons in the Cortex," 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, 2006, pp. 6702-6702, doi:10.1109/IEMBS.2006.260925.
  12. Y. Kim et al. "An analysis of learning performance changes in spiking neural networks(SNN)", The Journal of the Convergence on Culture Technology (JCCT) Vol. 6, No. 3, pp.465-470, August 31, 2020. pISSN 2384-0358, eISSN 2384-0366
  13. T.S. Chou, H.J. Kashyap, J. Xing, S. Listopad, E. Rounds, M. Beyeler, N. Dutt, and J.L. Krichmar "CARLsim 4: An Open Source Library for Large Scale, Biologically Detailed Spiking Neural Network Simulation using Heterogeneous Clusters" International Joint Conference on Neural Networks 2018
  14. N.T. Carnevale and M.L. Hines The NEURON Book. Cambridge, UK: Cambridge University Press, 2006.
  15. D. F. M. Goodman and R. Brette, "The Brian simulator," Frontiers in Computational Neuroscience, Sep. 2009.
  16. C. H. Papadimitriou "The complexity of the Lin-Kernighan heuristic for the travelling salesman problem". SIAM Journal on Computing. 21 (3): 450-465. doi:10.1137/0221030.
  17. Y. LeCun, L. Bottou, Y. Bengio, and P., Haffner, "Gradient-based learning applied to document recognition." Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998. https://doi.org/10.1109/5.726791