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Bio-inspired Spin-Torque Computing Architectures

Periodic Reporting for period 2 - bioSPINspired (Bio-inspired Spin-Torque Computing Architectures)

Reporting period: 2018-03-01 to 2019-08-31

THE AIM OF THE BIOSPINSPIRED PROJECT IS TO DEMONSTRATE THAT COUPLED SPIN TORQUE NANODEVICES CAN REVOLUTIONIZE BIO-INSPIRED COMPUTING. We intend to show that they provide the key to implementing abstract computing concepts inspired by the non-linear dynamics of the brain, concepts that for the most part have only been modelled until now. To achieve this goal, I will make all the scientific progress that is needed: material science for spin-torque nanodevices, physics of the dynamical coupling between these devices, bio-inspired computational models based on the hardware implementation of non-linear dynamics, and computing devices built from complex networks of interconnected spintronic neurons and synapses.

Biological systems have impressive computing abilities. We, humans, are able to recognize people we barely know in just a fraction of second, even in a crowd. And we do this incredibly complex task with 104 times less power than any supercomputer! It therefore makes sense to take inspiration from biology to build data processing systems to perform low power ‘cognitive’ tasks on-chip and complement our traditional microprocessors . Today, incredible advances towards understanding the way the brain computes have been made, and the machine-learning community is developing impressively efficient ‘brain-like’ methods to perform cognitive tasks. Deep Neural Networks are now the working principle of virtual assistants on smartphones, and used for a wide range of classification tasks . Indeed, we need to invent new ways to rapidly and automatically make sense of the massive amount of digital information we generate every day, and neural networks are intrinsically suited for such cognitive tasks.

However neural networks cannot be satisfying in their present software version. It is the massively parallel, analog and relatively uniform architecture of biological systems that confers them their greatest assets: speed, low energy consumption and tolerance to defects. When mapped on the sequential architecture of our computers, bio-inspired algorithms lose these precious qualities, and suffer from the excessive energy dissipation that limits the performances of our processors. Building bio-inspired hardware is therefore extremely relevant today due to their application scope and low energy consumption .
Nanodevices for bio-inspired computing. As biological systems, bio-inspired hardware should be composed of a huge number of computing nodes and connections in order to be efficient (there are 10^11 neurons and 10^15 synapses in the brain). While CMOS technology might appear as the most mature technique to build such systems, it suffers from the high number of transistors required to imitate synapses and neurons, and the related power dissipation issues. Fabricating devices that can emulate synapses and neurons at the nanoscale therefore appears as the key for the development of dense, efficient bio-inspired chips. But this requires adapting the existing abstract bio-inspired computing models to specific hardware implementations. The materials, the physics that will allow nanodevices to embody interesting functions, the overall hybrid CMOS-nanodevice architecture and the bio-inspired computing models need to be thought together.

Neurons and synapses are dynamical objects. The synapses ability to transmit information is modulated in time according to the activity of the neurons that they interconnect, which allows the network to learn. Neurons can be modelled as nonlinear oscillators that adjust their rhythms depending on incoming signals . The brain itself displays a wealth of phenomena characteristic of non-linear dynamical systems: synchronization of oscillating neural assemblies , criticality and even chaotic behaviour . Computing models called Recurrent Neural Networks take inspiration from these rich brain dynamics for performing data processing. Recurrent neural network have incredible computing capabilities and can imp
In the first 36 months of the project we have shown that spin-torque nano-oscillators can be used as artifical neurons.We have demonstrated that the non-linear response of spin-torque nano-oscillators combined with their stability and long life time enables neuromorphic computing through two key results, both independently published in the journal Nature. First, wehave shown experimentally in 2017 that a single nano-oscillator has the desired properties to emulate neurons despite their small size and the related sensitivity to noise. We have used time-multiplexing to emulate a full neural network that was able to recognize spoken digits from 0 to 9 with a precision up to 99.6 %, which is as good as much larger neurons and software simulations. Then, we have shown in 2018 that a small hardware network composed of two layers of RF neurons could communicate through their microwave emissions and recognize spoken vowels. This result proves that spintronic neurons can be interconnected through microwaves and that they can learn through their coupled dynamics.
In the future we will build larger and deeper networks of tehse nanodevices and we will investigate how to use their rich physics for computing as efficiently as possible.
Four spintronic oscillators learn to recognize vowels