Brain-inspired computing with spin torque nano-oscillators
Dr. Jacob Torrejon
Univ. Paris-Sud & Paris-Saclay, 91767 Palaiseau, France
Date & Time: 16:30 - 17:30, September 25th (Mon), 2017.
Place: 8F Medium Seminar Room(#811-812), Sengen.
Abstract:
Nowadays the amount of generated data increases dramatically and thus new computing paradigms are needed. For many tasks such as face or speech recognition, the brain processes the information in a much faster and much more power efficient way than any classical computers. The brain displays many signatures of non-linear dynamical behaviour, such as synchronization or complex transient behaviour [1,2]. These observations have inspired a whole class of models that harness the complex non-linear dynamical networks to perform neuro-inspired computing [3]. In this framework, neurons are modelled as non-linear oscillators, and synapses as the coupling between oscillators. These abstract models are very good at processing waveforms for pattern recognition but there are very few hardware implementations based on networks of coupled oscillators. This type of computing requires a huge number of oscillators for achieving excellent performance and nanoscale oscillators are necessary for easy integration in a microchip. However small devices tend to be noisy and to lack the stability required to process data in a reliable way.
Spin torque nano-oscillators are a promising solution to over this issue because their well-controlled magnetization dynamics can lead to high signal to noise ratios. In addition, the other main advantages of spintronic oscillators compared to others existing oscillators are their exceptional ability to interact, non-linear tunability, fast time response (ns range), long lifetime and lower power consumption [4]. In this seminar, I will show how to leverage the non-linear dynamics of spin-torque nano-oscillators for neuromorphic computing, and present our first experimental achievement of speech recognition task [5]. Finally, I will give the main ingredients towards massively parallel architectures.
[1] D. R. Chialvo, Nat. Phys. 6, 744 (2010).
[2] J. Fell and N. Axmacher, Nat. Rev. Neurosci. 12, 105 (2011).
[3] E. M. Izhikevich, IEEE Trans. Neural Networks 15, 1063 (2004).
[4] J. Grollier, D. Querlioz, and M. D. Stiles, Proc. IEEE 104, 2024 (2016).
[5] J. Torrejon et al, Nature 547, 428 (2017).
This project is funded by European Research Council (ERC) grant, bioSPINspired 682955
Spin torque nano-oscillators are a promising solution to over this issue because their well-controlled magnetization dynamics can lead to high signal to noise ratios. In addition, the other main advantages of spintronic oscillators compared to others existing oscillators are their exceptional ability to interact, non-linear tunability, fast time response (ns range), long lifetime and lower power consumption [4]. In this seminar, I will show how to leverage the non-linear dynamics of spin-torque nano-oscillators for neuromorphic computing, and present our first experimental achievement of speech recognition task [5]. Finally, I will give the main ingredients towards massively parallel architectures.
[1] D. R. Chialvo, Nat. Phys. 6, 744 (2010).
[2] J. Fell and N. Axmacher, Nat. Rev. Neurosci. 12, 105 (2011).
[3] E. M. Izhikevich, IEEE Trans. Neural Networks 15, 1063 (2004).
[4] J. Grollier, D. Querlioz, and M. D. Stiles, Proc. IEEE 104, 2024 (2016).
[5] J. Torrejon et al, Nature 547, 428 (2017).
This project is funded by European Research Council (ERC) grant, bioSPINspired 682955