Vowel recognition with four coupled spin-torque nano-oscillators

  1. Romera, Miguel 1
  2. Talatchian, Philippe
  3. Tsunegi, Sumito
  4. Abreu Araujo, Flavio
  5. Cros, Vincent
  6. Bortolotti, Paolo
  7. Trastoy, Juan
  8. Yakushiji, Kay
  9. Fukushima, Akio
  10. Kubota, Hitoshi
  11. Yuasa, Shinji
  12. Ernoult, Maxence
  13. Vodenicarevic, Damir
  14. Hirtzlin, Tifenn
  15. Locatelli, Nicolas
  16. Querlioz, Damien
  17. Grollier, Julie
  1. 1 1Unité Mixte de Physique, CNRS, Thales, Université Paris-Sud, Université Paris-Saclay, Palaiseau, France
Revista:
Nature

ISSN: 0028-0836,  1476-4687

Año de publicación: 2018

Volumen: 563

Número: 7730

Páginas: 230-234

Tipo: Artículo

DOI: 10.1038/S41586-018-0632-Y GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Nature

Resumen

In recent years, artificial neural networks have become the flagship algorithm of artificial intelligence1. In these systems, neuron activation functions are static, and computing is achieved through standard arithmetic operations. By contrast, a prominent branch of neuroinspired computing embraces the dynamical nature of the brain and proposes to endow each component of a neural network with dynamical functionality, such as oscillations, and to rely on emergent physical phenomena, such as synchronization2,3,4,5,6, for solving complex problems with small networks7,8,9,10,11. This approach is especially interesting for hardware implementations, because emerging nanoelectronic devices can provide compact and energy-efficient nonlinear auto-oscillators that mimic the periodic spiking activity of biological neurons12,13,14,15,16. The dynamical couplings between oscillators can then be used to mediate the synaptic communication between the artificial neurons. One challenge for using nanodevices in this way is to achieve learning, which requires fine control and tuning of their coupled oscillations17; the dynamical features of nanodevices can be difficult to control and prone to noise and variability18. Here we show that the outstanding tunability of spintronic nano-oscillators—that is, the possibility of accurately controlling their frequency across a wide range, through electrical current and magnetic field—can be used to address this challenge. We successfully train a hardware network of four spin-torque nano-oscillators to recognize spoken vowels by tuning their frequencies according to an automatic real-time learning rule. We show that the high experimental recognition rates stem from the ability of these oscillators to synchronize. Our results demonstrate that non-trivial pattern classification tasks can be achieved with small hardware neural networks by endowing them with nonlinear dynamical features such as oscillations and synchronization.

Referencias bibliográficas

  • Silver, D. et al. Mastering the game of Go without human knowledge. Nature 550, 354–359 (2017).
  • Borisyuk, R., Denham, M., Hoppensteadt, F., Kazanovich, Y. & Vinogradova, O. An oscillatory neural network model of sparse distributed memory and novelty detection. Biosystems 58, 265–272 (2000).
  • Jaeger, H. & Haas, H. Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304, 78–80 (2004).
  • Rabinovich, M., Huerta, R. & Laurent, G. Transient dynamics for neural processing. Science 321, 48–50 (2008).
  • Sussillo, D. Neural circuits as computational dynamical systems. Curr. Opin. Neurobiol. 25, 156–163 (2014).
  • Pikovsky, A. & Rosenblum, M. Dynamics of globally coupled oscillators: progress and perspectives. Chaos 25, 097616 (2015).
  • Kumar, S., Strachan, J. P. & Williams, R. S. Chaotic dynamics in nanoscale NbO2 Mott memristors for analogue computing. Nature 548, 318–321 (2017).
  • Csaba, G. & Porod, W. Computational study of spin-torque oscillator interactions for non-Boolean computing applications. IEEE Trans. Magn. 49, 4447–4451 (2013).
  • Yogendra, K., Fan, D., Jung, B. & Roy, K. Magnetic pattern recognition using injection-locked spin-torque nano-oscillators. IEEE Trans. Electron Dev. 63, 1674–1680 (2016).
  • Macià, F., Kent, A. D. & Hoppensteadt, F. C. Spin-wave interference patterns created by spin-torque nano-oscillators for memory and computation. Nanotechnology 22, 095301 (2011).
  • Fang, Y., Yashin, V. V., Levitan, S. P. & Balazs, A. C. Pattern recognition with “materials that compute”. Sci. Adv. 2, e1601114 (2016).
  • Pickett, M. D., Medeiros-Ribeiro, G. & Williams, R. S. A scalable neuristor built with Mott memristors. Nat. Mater. 12, 114–117 (2013).
  • Pufall, M. R. et al. Physical implementation of coherently coupled oscillator networks. IEEE J. Explor. Solid-State Comput. Devices Circuits 1, 76–84 (2015).
  • Sharma, A. A., Bain, J. A. & Weldon, J. A. Phase coupling and control of oxide-based oscillators for neuromorphic computing. IEEE J. Explor. Solid-State Comput. Devices Circuits 1, 58–66 (2015).
  • Grollier, J., Querlioz, D. & Stiles, M. D. Spintronic nanodevices for bioinspired computing. Proc. IEEE 104, 2024–2039 (2016).
  • Parihar, A., Shukla, N., Jerry, M., Datta, S. & Raychowdhury, A. Computational paradigms using oscillatory networks based on state-transition devices. In 2017 International Joint Conference on Neural Networks (IJCNN) 3415–3422 (IEEE, 2017).
  • Vassilieva, E., Pinto, G., de Barros, J. A. & Suppes, P. Learning pattern recognition through quasi-synchronization of phase oscillators. IEEE Trans. Neural Netw. 22, 84–95 (2011).
  • Vodenicarevic, D., Locatelli, N., Araujo, F. A., Grollier, J. & Querlioz, D. A nanotechnology-ready computing scheme based on a weakly coupled oscillator network. Sci. Rep. 7, 44772 (2017).
  • Locatelli, N., Cros, V. & Grollier, J. Spin-torque building blocks. Nat. Mater. 13, 11–20 (2014).
  • Slavin, A. & Tiberkevich, V. Nonlinear auto-oscillator theory of microwave generation by spin-polarized current. IEEE Trans. Magn. 45, 1875–1918 (2009).
  • Kaka, S. et al. Mutual phase-locking of microwave spin torque nano-oscillators. Nature 437, 389–392 (2005).
  • Mancoff, F. B., Rizzo, N. D., Engel, B. N. & Tehrani, S. Phase-locking in double-point-contact spin-transfer devices. Nature 437, 393–395 (2005).
  • Houshang, A. et al. Spin-wave-beam driven synchronization of nanocontact spin-torque oscillators. Nat. Nanotech. 11, 280–286 (2016).
  • Lebrun, R. et al. Mutual synchronization of spin torque nano-oscillators through a long-range and tunable electrical coupling scheme. Nat. Commun. 8, 15825 (2017).
  • Torrejon, J. et al. Neuromorphic computing with nanoscale spintronic oscillators. Nature 547, 428–431 (2017).
  • Tsunegi, S. et al. High emission power and Q factor in spin torque vortex oscillator consisting of FeB free layer. Appl. Phys. Express 7, 063009 (2014).
  • Romera, M. et al. Enhancing the injection locking range of spin torque oscillators through mutual coupling. Appl. Phys. Lett. 109, 252404 (2016).
  • Hillenbrand, J., Getty, L. A., Wheeler, K. & Clark, M. J. Acoustic characteristics of American English vowels. J. Acoust. Soc. Am. 97, 3099–3111 (1994).
  • Vodenicarevic, D., Locatelli, N., Grollier, J. & Querlioz, D. Synchronization detection in networks of coupled oscillators for pattern recognition. In 2016 International Joint Conference on Neural Networks (IJCNN) 2015–2022 (IEEE, 2016).
  • Fang, B. et al. Giant spin-torque diode sensitivity in the absence of bias magnetic field. Nat. Commun. 7, 11259 (2016).
  • Tulapurkar, A. A. et al. Spin-torque diode effect in magnetic tunnel junctions. Nature 438, 339–342 (2005).
  • Louis, S. et al. Low power microwave signal detection with a spin-torque nano-oscillator in the active self-oscillating regime. IEEE Trans. Magn. 53, 1–4 (2017).
  • Jouppi, N. P. et al. Datacenter performance analysis of a tensor processing unit. In Proc. 44th Annual International Symposium on Computer Architecture 1–12 (ACM, 2017).
  • Livi, P. & Indiveri, G. A current-mode conductance-based silicon neuron for address-event neuromorphic systems. In 2009 IEEE International Symposium on Circuits and Systems 2898–2901 (IEEE, 2009).
  • Qiao, N. & Indiveri, G. Scaling mixed-signal neuromorphic processors to 28 nm FD-SOI technologies. In 2016 IEEE Biomedical Circuits and Systems Conference (BioCAS) 552–555 (IEEE, 2016).
  • Wijekoon, J. H. B. & Dudek, P. Compact silicon neuron circuit with spiking and bursting behaviour. Neural Netw. 21, 524–534 (2008).
  • Tran, D. X. & Dang, T. T. An ultra-low power consumption and very compact 1.49 GHz CMOS voltage controlled ring oscillator. In 2014 International Conference on Advanced Technologies for Communications (ATC 2014) 239–244 (IEEE, 2014).
  • Tomita, Y. et al. An 8-to-16GHz 28nm CMOS clock distribution circuit based on mutual-injection-locked ring oscillators. In 2013 Symposium on VLSI Circuits C238–C239 (IEEE, 2013).
  • Gajek, M. et al. Spin torque switching of 20 nm magnetic tunnel junctions with perpendicular anisotropy. Appl. Phys. Lett. 100, 132408 (2012).