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SPIKING NEURAL NETWORKS

Sparsity provides a competitive advantage

Neuromorphic chips that use spikes to encode information could provide fast and energy-efficient computing for ubiquitous embedded systems. A bio-plausible spike-timing solution for training spiking neural networks that makes the most of sparsity is implemented on the BrainScaleS-2 hardware platform.

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Fig. 1: TTFS encoding and deployment in neuromorphic hardware.

References

  1. 1.

    Indiveri, G. & Liu, S.-C. Proc. IEEE 103, 1379–1397 (2015).

    Article  Google Scholar 

  2. 2.

    Davies, M. et al. Proc. IEEE 109, 911–934 (2021).

    Article  Google Scholar 

  3. 3.

    Frenkel, C., Bol, D. & Indiveri, G. Preprint at https://arxiv.org/abs/2106.01288 (2021).

  4. 4.

    Murmann, B. & Höfflinger, B. (eds). NANO-CHIPS 2030: On-chip AI for an Efficient Data-driven World (Springer, 2020).

  5. 5.

    Chicca, E., Stefanini, F., Bartolozzi, C. & Indiveri, G. Proc. IEEE 102, 1367–1388 (2014).

    Article  Google Scholar 

  6. 6.

    Göltz, J. et al. Nat. Mach. Intell. https://doi.org/10.1038/s42256-021-00388-x (2021).

  7. 7.

    Rueckauer, B., Lungu, I.-A., Hu, Y., Pfeiffer, M. & Liu, S.-C. Front. Neurosci. 11, 682 (2017).

    Article  Google Scholar 

  8. 8.

    Davidsol, S. & Furber, S. B. Front. Neurosci. 15, 651141 (2021).

    Article  Google Scholar 

  9. 9.

    Mostafa, H. IEEE Trans. Neural Netw. Learn. Syst. 29, 3227–3235 (2017).

    Google Scholar 

  10. 10.

    Kheradpisheh, S. R. & Masquelier, T. Int. J. Neural Syst. 30, 2050027 (2020).

    Article  Google Scholar 

  11. 11.

    Fourcaud-Trocmé, N., Hansel, D., Van Vreeswijk, C. & Brunel, N. J. Neurosci. 23, 11628–11640 (2003).

    Article  Google Scholar 

  12. 12.

    Schemmel, J., Billaudelle, S., Dauer, P. & Weis, J. Preprint at https://arxiv.org/abs/2003.11996 (2020).

  13. 13.

    Thorpe, S., Delorme, A. & Van Rullen, R. Neural Netw. 14, 715–725 (2001).

    Article  Google Scholar 

  14. 14.

    Frenkel, C., Legat, J.-D. & Bol, D. IEEE International Symposium on Circuits and Systems (ISCAS, 2020).

  15. 15.

    Indiveri, G. & Sandamirskaya, Y. IEEE Signal Process. Mag. 36, 16–28 (2019).

    Article  Google Scholar 

Download references

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Correspondence to Charlotte Frenkel.

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Frenkel, C. Sparsity provides a competitive advantage. Nat Mach Intell 3, 742–743 (2021). https://doi.org/10.1038/s42256-021-00387-y

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