Periodic Reporting for period 3 - bioSPINspired (Bio-inspired Spin-Torque Computing Architectures)
Reporting period: 2019-09-01 to 2021-02-28
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 implement any kind of dynamics (from fixed points to chaos) . Attractors can be leveraged to store memories. And transient dynamics can be used to process input time sequences provided by sensors or to generate trajectories as outputs for motor control .
In bioSPINspired, we want to show that spin torque nanodevices, which are multi-functional and tunable nonlinear dynamical nano-components, will be ideal building blocks for the hardware implementation of models that harness the power of complex non-linear dynamical recurrent networks for computing.