Three-Dimensional Multilayer Nanomagnetic Arrays for Neuromorphic Low-Energy Magnonic Processing

The energy cost of computing and artificial intelligence (AI) is spiraling out of control, forecast to reach 20.9% of global energy consumption by 2030. Training a neural net to robotically solve a Rubik's Cube toy consumed 2.8 GWh , while human brains consume just ~20 W. The recent successes of large machine learning models such as OpenAI's GPT-3 and Chat-GPT are accompanied by huge carbon footprints - Chat-GPT consumed $15 million in electricity during training & generated ~552 tons of CO2 . Its ongoing energy bill is estimated at ~$3 million/month, with accompanying levels of greenhouse emissions. This unsustainable energy consumption represents both a real barrier to reaching net-zero futures and a ceiling on the power of AI computing.

A big part of this problem is that we're currently trying to do brain-like computing with computers that are nothing like a brain. Today's computers use far more energy shuttling data between separate memory and processor units than actually processing, whereas neurons in the brain provide integrated memory and processing - a key driver for their radically lower energy cost. Consequently, there is a pressing need for hardware systems that function in a brain-like (neuromorphic) manner, storing and processing information natively in the same unit.

In many ways, nanomagnets behave a lot like neurons in the brain. They can react to the behaviour of surrounding magnets, flipping their poles from north to south similar to how neurons send jolts of electricity. Nanomagnets can remember what they've seen in the past and change their behaviour in response to this, learning from their experiences and gradually improving at tasks like voice recognition and pattern prediction. Nanomagnets provide both memory from their ability to remember data for 1000s of years (hard drives were originally made from nanomagnets for this reason), and processing from their ability to react nonlinearly to input data at GHz speeds - oscillating in a special way known as 'magnonics'.
Indeed, the maths powering modern software neural networks originate from theoretical frameworks developed by physicists in the 1970's to describe strongly-interacting magnetic networks . The early machine learning community adopted these frameworks (originally termed Hopfield networks ) and adapted & refined them into the neural networks of today.

Since the early successes of machine learning, engineers have dreamt of removing the software layer of abstraction and implementing machine learning directly in physical magnetic networks. However until recently, the engineering challenges of providing efficient data input and output schemes had prevented realisation of such systems. Our team have now solved these issues to accomplish the world-first example of neuromorphic computing in nanomagnetic arrays, using the magnon dynamics of a nanomagnetic array to process information and solve a range of AI tasks including future prediction of complex biological signals.

We now have a way to massively improve the power of our AI computation at no extra energy cost, by moving our nanomagnetic arrays from 2D into 3D structures, our early results and simulations show that our computing power is likely to radically increase. In this project, we will work between a group in the UK led by early-career researcher Jack Gartside and a group in the USA lead by world-expert Prof. Benjamin Jungfleisch to test our ideas & bring low-energy, low-carbon AI one step closer to reality.