Julie Grollier

CNRS, France

RESEARCH INTERESTS

Harnessing the multifunctionality of spintronics as a platform for neuromorphic physics.

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RESEARCH PROFILE

This short video illustrates the learning process for the spin-torque nano-oscillator based hardware. Here four coupled oscillators are trained to recognize seven American vowels. Each vowel is characterized by two frequencies, fA and fB, and is represented by a dot. The different vowels have different colors. The spread within each vowel is due to the different pronunciations of the 37 speakers.
Each vowel is applied to the oscillator system as the sum of two microwave magnetic fields which can phase-lock the oscillators. The different colors in the background map represent the different experimental oscillator synchronization configurations obtained for different input frequencies fA and fB. Recognition is achieved when each vowel cloud corresponds to a single synchronization configuration. At the beginning of the video, the oscillator network is randomly set, and the recognition rate is closed to zero.
At each training step, the dc current though each oscillator is modified through a learning rule which brings the oscillator frequency closer to the desired synchronization configuration associated to the vowels that have been applied. At the end of the video, after the networks has learned, the dots corresponding to each vowel are in majority encompassed in different regions of the background map corresponding to different synchronization configurations, which gives a recognition rate of 89% on the training data.

RESEARCH EQUIPMENT