Spin waves to the rescue: Development of a spintronic reservoir computing platform
Lead Research Organisation:
University of Oxford
Department Name: Oxford Physics
Abstract
As we approach the theoretical limit of 3 nm transistor channel lengths, manufacturing challenges of CMOS architectures become exponentially more difficult and more expensive to overcome. Simultaneously, a seismic shift is occurring in the computational workload, away from offline processing to real-time big-data applications driven by the Internet of Things (IoT), robotics and autonomous agents. This combination of factors has led to an intensified exploration of alternative computing methodologies that span the entire Boolean computational stack from physical effects, to materials, devices, architectures, and data representations. It also includes novel, non-Boolean methods of computing such as quantum, wave and neuromorphic computation, Boltzmann machines and others. Exactly which combination of computational elements will evolve from this plethora of options is far from clear. However, it is possible to state general requirements future computing platforms must meet. First, any new computing methodology must be compatible with the existing multi-trillion-pound infrastructure associated with current CMOS based computing. Second, it must be scalable through multiple generations of incremental hardware and software improvements. Third, the performance/cost metric must greatly exceed that of Boolean CMOS processors, and, fourth, the new technology must provide a much more energy-efficient alternative to existing technology.
Reservoir Computing (RC) leverages fast nonlinear dynamics in analogue physical systems to map a system's spontaneous transient response to solutions of traditionally hard problems such as classification tasks and signal prediction. This technique effectively ties memory and processing tasks to the intrinsic materials properties. The specific details of the physical system in which RC is implemented, however, are not relevant so long the following key criteria are met: dynamical non-linearity, high phase space dimensionality, uniquely reproducible initial state, easy out-of-equilibrium perturbation, and readability of dynamical state. The main quest is to identify a system suitable for the task, which is not plagued by real world-incompatible requirements. Our proposed solution is based on driven spin-wave excitations which guarantee both sufficiently complex transient responses, controlled chaoticity, as well as providing a natural spintronic platform for straightforward driving and reading of dynamical magnetic states. Our proposed work aims at demonstrating the versatility of spin-wave interference as the key candidate for the implementation of RC in a real-world device. We believe that spin-waves in magnetic nanostructures are ideal candidates for developing drop-in substitutes for circuit components, as well as stand-alone devices.
Success in this endeavour would prove groundbreaking for the development of real-time pattern detection technologies with the potential for high-impact deployment in areas ranging from medical monitoring to climate modelling. Complex pattern recognition tasks could be performed on RC hardware with square-micrometre surface area, 100 micro-W power consumption and 10 ns inference time. Compared to the server stacks currently used by industry leaders (Google, Apple, Facebook, etc.) to satisfy global demand, success in this action will pave the way for massively more resource efficient big-data solutions.
Reservoir Computing (RC) leverages fast nonlinear dynamics in analogue physical systems to map a system's spontaneous transient response to solutions of traditionally hard problems such as classification tasks and signal prediction. This technique effectively ties memory and processing tasks to the intrinsic materials properties. The specific details of the physical system in which RC is implemented, however, are not relevant so long the following key criteria are met: dynamical non-linearity, high phase space dimensionality, uniquely reproducible initial state, easy out-of-equilibrium perturbation, and readability of dynamical state. The main quest is to identify a system suitable for the task, which is not plagued by real world-incompatible requirements. Our proposed solution is based on driven spin-wave excitations which guarantee both sufficiently complex transient responses, controlled chaoticity, as well as providing a natural spintronic platform for straightforward driving and reading of dynamical magnetic states. Our proposed work aims at demonstrating the versatility of spin-wave interference as the key candidate for the implementation of RC in a real-world device. We believe that spin-waves in magnetic nanostructures are ideal candidates for developing drop-in substitutes for circuit components, as well as stand-alone devices.
Success in this endeavour would prove groundbreaking for the development of real-time pattern detection technologies with the potential for high-impact deployment in areas ranging from medical monitoring to climate modelling. Complex pattern recognition tasks could be performed on RC hardware with square-micrometre surface area, 100 micro-W power consumption and 10 ns inference time. Compared to the server stacks currently used by industry leaders (Google, Apple, Facebook, etc.) to satisfy global demand, success in this action will pave the way for massively more resource efficient big-data solutions.
Title | GPU micromagnetic simulation PC |
Description | We setup a GPU-based PC for the micromagnetic simulation of reservoir computing devices. The in-house test of simulation setups are crucial before running large, time-consuming tasks on supercomputer clusters. |
Type Of Material | Improvements to research infrastructure |
Year Produced | 2022 |
Provided To Others? | No |
Impact | Speed up of micromagnetic simulation tasks using Mumax3. |
URL | https://mumax.github.io/ |
Title | Sub-GHz radio-frequency setup for device characterization |
Description | We developed a setup for the radio-frequency characterization of devices relevant to reservoir computing. The setup is capable of covering the frequency range from DC to 500 MHz. The setup is used for surface acoustic wave device measurements, resulting in the discovery of reservoir computing capabilities in these devices. The work will result in a patent application (under preparation; submission in 2023). |
Type Of Material | Improvements to research infrastructure |
Year Produced | 2023 |
Provided To Others? | Yes |
Impact | Surface acoustic wave reservoir computing. |
Description | AGH University |
Organisation | AGH University of Science and Technology |
Country | Poland |
Sector | Academic/University |
PI Contribution | Discussions of the fabrication of magnetic tunnel junction devices, and their magnetotransport measurements. |
Collaborator Contribution | Contributions to the optimization of nanofabrication processes, such as deposition, lithography, and etching; as well as tunnel magnetoresistance measurements. |
Impact | Refinement of nanofabrication recipes. |
Start Year | 2022 |
Description | Support for grant - Prof Bluegel |
Organisation | Julich Research Centre |
Country | Germany |
Sector | Academic/University |
PI Contribution | Discussions and feedback cycle between experiment and theory (simulation work in Julich). |
Collaborator Contribution | Computer time (about 20000 hours/year) on GPU based cluster, TIER-1 (Jureka) and TIER-0 (JEWELS) supercomputers hosted at Forschungszentrum Jüich. Work of a highly experienced postdoctoral researcher (0.1 FTE/year) and Prof. Bluegel (0.05 FTE/year). |
Impact | Their simulations allowed us to refine our device design for the spin wave reservoir. |
Start Year | 2021 |