Neurons in the brain behave as nonlinear oscillators, which develop rhythmic activity and interact to process information. Taking inspiration from this behaviour to realize high-density, low-power neuromorphic computing will require very large numbers of nanoscale nonlinear oscillators. A simple estimation indicates that to fit 108 oscillators organized in a two-dimensional array inside a chip the size of a thumb, the lateral dimension of each oscillator must be smaller than one micrometre. However, nanoscale devices tend to be noisy and to lack the stability that is required to process data in a reliable way. For this reason, despite multiple theoretical proposals and several candidates, including memristive and superconducting oscillators, a proof of concept of neuromorphic computing using nanoscale oscillators has yet to be demonstrated. Here we show experimentally that a nanoscale spintronic oscillator (a magnetic tunnel junction) can be used to achieve spoken-digit recognition with an accuracy similar to that of state-of-the-art neural networks. We also determine the regime of magnetization dynamics that leads to the greatest performance. These results, combined with the ability of the spintronic oscillators to interact with each other, and their long lifetime and low energy consumption, open up a path to fast, parallel, on-chip computation based on networks of oscillators.

/pdf/neuromorphic-computing-with-nanoscale-spintronic-oscillators-3e3rewmz7x.pdf

Neuromorphic computing with nanoscale spintronic oscillators

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私の computer 環境

In spin Hall nano-oscillators (SHNOs), pure spin currents drive local regions of magnetic films and nanostructures into auto-oscillating precession. If such regions are placed in close proximity to each other they can interact and may mutually synchronize. Here, we demonstrate robust mutual synchronization of two-dimensional SHNO arrays ranging from 2 × 2 to 8 × 8 nano-constrictions, observed both electrically and using micro-Brillouin light scattering microscopy. On short time scales, where the auto-oscillation linewidth $$\Delta f$$ is governed by white noise, the signal quality factor, $$Q=f/\Delta f$$, increases linearly with the number of mutually synchronized nano-constrictions (N), reaching 170,000 in the largest arrays. We also show that SHNO arrays exposed to two independently tuned microwave frequencies exhibit the same synchronization maps as can be used for neuromorphic vowel recognition. Our demonstrations may hence enable the use of SHNO arrays in two-dimensional oscillator networks for high-quality microwave signal generation and ultra-fast neuromorphic computing. Synchronization of oscillators can be used to carry out cognitive tasks. Large two-dimensional arrays of synchronized spin Hall nano-oscillators have now been demonstrated, and may in future enable neuromorphic computing on the nanoscale.

/pdf/two-dimensional-mutually-synchronized-spin-hall-nano-22zy3vcis4.pdf

Two-dimensional mutually synchronized spin Hall nano-oscillator arrays for neuromorphic computing

Neuromorphic computing promises to markedly improve the efficiency of certain computational tasks, such as perception and decision-making. Although software and specialized hardware implementations of neural networks have made tremendous accomplishments, both implementations are still many orders of magnitude less energy efficient than the human brain. We demonstrate a new form of artificial synapse based on dynamically reconfigurable superconducting Josephson junctions with magnetic nanoclusters in the barrier. The spiking energy per pulse varies with the magnetic configuration, but in our demonstration devices, the spiking energy is always less than 1 aJ. This compares very favorably with the roughly 10 fJ per synaptic event in the human brain. Each artificial synapse is composed of a Si barrier containing Mn nanoclusters with superconducting Nb electrodes. The critical current of each synapse junction, which is analogous to the synaptic weight, can be tuned using input voltage spikes that change the spin alignment of Mn nanoclusters. We demonstrate synaptic weight training with electrical pulses as small as 3 aJ. Further, the Josephson plasma frequencies of the devices, which determine the dynamical time scales, all exceed 100 GHz. These new artificial synapses provide a significant step toward a neuromorphic platform that is faster, more energy-efficient, and thus can attain far greater complexity than has been demonstrated with other technologies.

https://advances.sciencemag.org/content/advances/4/1/e1701329.full.pdf

Ultralow power artificial synapses using nanotextured magnetic Josephson junctions.

Coupled oscillators are highly complex dynamical systems, and it is an intriguing concept to use this oscillator dynamics for computation. The idea is not new, but is currently the subject to intense research as part of the quest for “beyond Moore” electronic devices. To a large extent, these efforts are motivated by biological observations: neural systems and mammalian brains, which seem to operate on oscillatory signals. In this paper, we give a survey of oscillator-based computing, with the goal of understanding its promise and limitation for next-generation computing. Our focus will be on the physics of (mostly nanoscale) oscillatory systems and on their characteristics that may enable effective computing.

Coupled oscillators for computing: A review and perspective

Conventional digital computation is rapidly approaching physical limits for speed and energy dissipation. Here we fabricate and test a simple neuromorphic circuit that models neuronal somas, axons, and synapses with superconducting Josephson junctions. The circuit models two mutually coupled excitatory neurons. In some regions of parameter space the neurons are desynchronized. In others, the Josephson neurons synchronize in one of two states, in-phase or antiphase. An experimental alteration of the delay and strength of the connecting synapses can toggle the system back and forth in a phase-flip bifurcation. Firing synchronization states are calculated g70 000 times faster than conventional digital approaches. With their speed and low energy dissipation (${10}^{\ensuremath{-}17}\phantom{\rule{0.16em}{0ex}}\mathrm{J}/\mathrm{spike}$), this set of proof-of-concept experiments establishes Josephson junction neurons as a viable approach for improvements in neuronal computation as well as applications in neuromorphic computing.

https://link.aps.org/accepted/10.1103/PhysRevE.95.032220

Steven Kaplan

Papers

Synchronization dynamics on the picosecond time scale in coupled Josephson junction neurons