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Laydevant J, Marković D, Grollier J. Training an Ising machine with equilibrium propagation. Nat Commun 2024; 15:3671. [PMID: 38693108 PMCID: PMC11063034 DOI: 10.1038/s41467-024-46879-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 03/12/2024] [Indexed: 05/03/2024] Open
Abstract
Ising machines, which are hardware implementations of the Ising model of coupled spins, have been influential in the development of unsupervised learning algorithms at the origins of Artificial Intelligence (AI). However, their application to AI has been limited due to the complexities in matching supervised training methods with Ising machine physics, even though these methods are essential for achieving high accuracy. In this study, we demonstrate an efficient approach to train Ising machines in a supervised way through the Equilibrium Propagation algorithm, achieving comparable results to software-based implementations. We employ the quantum annealing procedure of the D-Wave Ising machine to train a fully-connected neural network on the MNIST dataset. Furthermore, we demonstrate that the machine's connectivity supports convolution operations, enabling the training of a compact convolutional network with minimal spins per neuron. Our findings establish Ising machines as a promising trainable hardware platform for AI, with the potential to enhance machine learning applications.
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Affiliation(s)
- Jérémie Laydevant
- Laboratoire Albert Fert, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France.
| | - Danijela Marković
- Laboratoire Albert Fert, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France
| | - Julie Grollier
- Laboratoire Albert Fert, CNRS, Thales, Université Paris-Saclay, 91767, Palaiseau, France.
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2
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van Weerdenburg WJ, Osterhage H, Christianen R, Junghans K, Domínguez E, Kappen HJ, Khajetoorians AA. Stochastic Syncing in Sinusoidally Driven Atomic Orbital Memory. ACS NANO 2024; 18:4840-4846. [PMID: 38291572 PMCID: PMC10867893 DOI: 10.1021/acsnano.3c09635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 01/11/2024] [Accepted: 01/25/2024] [Indexed: 02/01/2024]
Abstract
Stochastically fluctuating multiwell systems are a promising route toward physical implementations of energy-based machine learning and neuromorphic hardware. One of the challenges is finding tunable material platforms that exhibit such multiwell behavior and understanding how complex dynamic input signals influence their stochastic response. One such platform is the recently discovered atomic Boltzmann machine, where each stochastic unit is represented by a binary orbital memory state of an individual atom. Here, we investigate the stochastic response of binary orbital memory states to sinusoidal input voltages. Using scanning tunneling microscopy, we investigated orbital memory derived from individual Fe and Co atoms on black phosphorus. We quantify the state residence times as a function of various input parameters such as frequency, amplitude, and offset voltage. The state residence times for both species, when driven by a sinusoidal signal, exhibit synchronization that can be quantitatively modeled by a Poisson process based on the switching rates in the absence of a sinusoidal signal. For individual Fe atoms, we also observe a frequency-dependent response of the state favorability, which can be tuned by the input parameters. In contrast to Fe, there is no significant frequency dependence in the state favorability for individual Co atoms. Based on the Poisson model, the difference in the response of the state favorability can be traced to the difference in the voltage-dependent switching rates of the two different species. This platform provides a tunable way to induce population changes in stochastic systems and provides a foundation toward understanding driven stochastic multiwell systems.
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Affiliation(s)
| | - Hermann Osterhage
- Institute
for Molecules and Materials, Radboud University, 6525 AJ Nijmegen, The Netherlands
| | - Ruben Christianen
- Institute
for Molecules and Materials, Radboud University, 6525 AJ Nijmegen, The Netherlands
| | - Kira Junghans
- Institute
for Molecules and Materials, Radboud University, 6525 AJ Nijmegen, The Netherlands
| | - Eduardo Domínguez
- Donders
Institute for Neuroscience, Radboud University, 6525 AJ Nijmegen, The Netherlands
| | - Hilbert J. Kappen
- Donders
Institute for Neuroscience, Radboud University, 6525 AJ Nijmegen, The Netherlands
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Ross A, Leroux N, De Riz A, Marković D, Sanz-Hernández D, Trastoy J, Bortolotti P, Querlioz D, Martins L, Benetti L, Claro MS, Anacleto P, Schulman A, Taris T, Begueret JB, Saïghi S, Jenkins AS, Ferreira R, Vincent AF, Mizrahi FA, Grollier J. Multilayer spintronic neural networks with radiofrequency connections. NATURE NANOTECHNOLOGY 2023; 18:1273-1280. [PMID: 37500772 DOI: 10.1038/s41565-023-01452-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 06/12/2023] [Indexed: 07/29/2023]
Abstract
Spintronic nano-synapses and nano-neurons perform neural network operations with high accuracy thanks to their rich, reproducible and controllable magnetization dynamics. These dynamical nanodevices could transform artificial intelligence hardware, provided they implement state-of-the-art deep neural networks. However, there is today no scalable way to connect them in multilayers. Here we show that the flagship nano-components of spintronics, magnetic tunnel junctions, can be connected into multilayer neural networks where they implement both synapses and neurons thanks to their magnetization dynamics, and communicate by processing, transmitting and receiving radiofrequency signals. We build a hardware spintronic neural network composed of nine magnetic tunnel junctions connected in two layers, and show that it natively classifies nonlinearly separable radiofrequency inputs with an accuracy of 97.7%. Using physical simulations, we demonstrate that a large network of nanoscale junctions can achieve state-of-the-art identification of drones from their radiofrequency transmissions, without digitization and consuming only a few milliwatts, which constitutes a gain of several orders of magnitude in power consumption compared to currently used techniques. This study lays the foundation for deep, dynamical, spintronic neural networks.
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Affiliation(s)
- Andrew Ross
- Unité Mixte de Physique CNRS/Thales, CNRS, Thales, Université Paris-Saclay, Palaiseau, France
| | - Nathan Leroux
- Unité Mixte de Physique CNRS/Thales, CNRS, Thales, Université Paris-Saclay, Palaiseau, France
| | - Arnaud De Riz
- Unité Mixte de Physique CNRS/Thales, CNRS, Thales, Université Paris-Saclay, Palaiseau, France
| | - Danijela Marković
- Unité Mixte de Physique CNRS/Thales, CNRS, Thales, Université Paris-Saclay, Palaiseau, France
| | - Dédalo Sanz-Hernández
- Unité Mixte de Physique CNRS/Thales, CNRS, Thales, Université Paris-Saclay, Palaiseau, France
| | - Juan Trastoy
- Unité Mixte de Physique CNRS/Thales, CNRS, Thales, Université Paris-Saclay, Palaiseau, France
| | - Paolo Bortolotti
- Unité Mixte de Physique CNRS/Thales, CNRS, Thales, Université Paris-Saclay, Palaiseau, France
| | - Damien Querlioz
- Centre de Nanosciences et de Nanotechnologies, Université Paris-Saclay, CNRS, Palaiseau, France
| | - Leandro Martins
- International Iberian Nanotechnology Laboratory (INL), Braga, Portugal
| | - Luana Benetti
- International Iberian Nanotechnology Laboratory (INL), Braga, Portugal
| | - Marcel S Claro
- International Iberian Nanotechnology Laboratory (INL), Braga, Portugal
| | - Pedro Anacleto
- International Iberian Nanotechnology Laboratory (INL), Braga, Portugal
| | | | - Thierry Taris
- Laboratoire de l'Intégration du Matériau au Système (IMS; UMR 5218), Univ. Bordeaux, CNRS, Bordeaux INP, Talence, France
| | - Jean-Baptiste Begueret
- Laboratoire de l'Intégration du Matériau au Système (IMS; UMR 5218), Univ. Bordeaux, CNRS, Bordeaux INP, Talence, France
| | - Sylvain Saïghi
- Laboratoire de l'Intégration du Matériau au Système (IMS; UMR 5218), Univ. Bordeaux, CNRS, Bordeaux INP, Talence, France
| | - Alex S Jenkins
- International Iberian Nanotechnology Laboratory (INL), Braga, Portugal
| | - Ricardo Ferreira
- International Iberian Nanotechnology Laboratory (INL), Braga, Portugal
| | - Adrien F Vincent
- Laboratoire de l'Intégration du Matériau au Système (IMS; UMR 5218), Univ. Bordeaux, CNRS, Bordeaux INP, Talence, France
| | - Frank Alice Mizrahi
- Unité Mixte de Physique CNRS/Thales, CNRS, Thales, Université Paris-Saclay, Palaiseau, France.
| | - Julie Grollier
- Unité Mixte de Physique CNRS/Thales, CNRS, Thales, Université Paris-Saclay, Palaiseau, France.
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Lu B, Fan CR, Liu L, Wen K, Wang C. Speed-up coherent Ising machine with a spiking neural network. OPTICS EXPRESS 2023; 31:3676-3684. [PMID: 36785354 DOI: 10.1364/oe.479903] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Coherent Ising machine (CIM) is a hardware solver that simulates the Ising model and finds optimal solutions to combinatorial optimization problems. However, for practical tasks, the computational process may be trapped in local minima, which is a key challenge for CIM. In this work, we design a CIM structure with a spiking neural network by adding dissipative pulses, which are anti-symmetrically coupled to the degenerate optical parametric oscillator pulses in CIM with a measurement feedback system. We find that the unstable oscillatory region of the spiking neural network could assist the CIM to escape from the trapped local minima. Moreover, we show that the machine has a different search mechanism than CIM, which can achieve a higher solution success probability and speed-up effect.
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CMOS-compatible ising machines built using bistable latches coupled through ferroelectric transistor arrays. Sci Rep 2023; 13:1515. [PMID: 36707539 PMCID: PMC9883258 DOI: 10.1038/s41598-023-28217-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 01/16/2023] [Indexed: 01/28/2023] Open
Abstract
Realizing compact and scalable Ising machines that are compatible with CMOS-process technology is crucial to the effectiveness and practicality of using such hardware platforms for accelerating computationally intractable problems. Besides the need for realizing compact Ising spins, the implementation of the coupling network, which describes the spin interaction, is also a potential bottleneck in the scalability of such platforms. Therefore, in this work, we propose an Ising machine platform that exploits the novel behavior of compact bi-stable CMOS-latches (cross-coupled inverters) as classical Ising spins interacting through highly scalable and CMOS-process compatible ferroelectric-HfO2-based Ferroelectric FETs (FeFETs) which act as coupling elements. We experimentally demonstrate the prototype building blocks of this system, and evaluate the scaling behavior of the system using simulations. Our work not only provides a pathway to realizing CMOS-compatible designs but also to overcoming their scaling challenges.
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Precise atom manipulation through deep reinforcement learning. Nat Commun 2022; 13:7499. [PMID: 36470857 PMCID: PMC9722711 DOI: 10.1038/s41467-022-35149-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 11/18/2022] [Indexed: 12/12/2022] Open
Abstract
Atomic-scale manipulation in scanning tunneling microscopy has enabled the creation of quantum states of matter based on artificial structures and extreme miniaturization of computational circuitry based on individual atoms. The ability to autonomously arrange atomic structures with precision will enable the scaling up of nanoscale fabrication and expand the range of artificial structures hosting exotic quantum states. However, the a priori unknown manipulation parameters, the possibility of spontaneous tip apex changes, and the difficulty of modeling tip-atom interactions make it challenging to select manipulation parameters that can achieve atomic precision throughout extended operations. Here we use deep reinforcement learning (DRL) to control the real-world atom manipulation process. Several state-of-the-art reinforcement learning (RL) techniques are used jointly to boost data efficiency. The DRL agent learns to manipulate Ag adatoms on Ag(111) surfaces with optimal precision and is integrated with path planning algorithms to complete an autonomous atomic assembly system. The results demonstrate that state-of-the-art DRL can offer effective solutions to real-world challenges in nanofabrication and powerful approaches to increasingly complex scientific experiments at the atomic scale.
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Atomic-scale visualization of chiral charge density wave superlattices and their reversible switching. Nat Commun 2022; 13:1843. [PMID: 35383190 PMCID: PMC8983771 DOI: 10.1038/s41467-022-29548-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 03/16/2022] [Indexed: 11/08/2022] Open
Abstract
Chirality is essential for various phenomena in life and matter. However, chirality and its switching in electronic superlattices, such as charge density wave (CDW) superlattices, remain elusive. In this study, we characterize the chirality switching with atom-resolution imaging in a single-layer NbSe2 CDW superlattice by the technique of scanning tunneling microscopy. The atomic arrangement of the CDW superlattice is found continuous and intact although its chirality is switched. Several intermediate states are tracked by time-resolved imaging, revealing the fast and dynamic chirality transition. Importantly, the switching is reversibly realized with an external electric field. Our findings unveil the delicate switching process of chiral CDW superlattice in a two-dimensional (2D) crystal down to the atomic scale.
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Knol EJ, Kiraly B, Rudenko AN, van Weerdenburg WMJ, Katsnelson MI, Khajetoorians AA. Gating Orbital Memory with an Atomic Donor. PHYSICAL REVIEW LETTERS 2022; 128:106801. [PMID: 35333070 DOI: 10.1103/physrevlett.128.106801] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 11/17/2021] [Accepted: 01/21/2022] [Indexed: 06/14/2023]
Abstract
Orbital memory is defined by two stable valencies that can be electrically switched and read out. To explore the influence of an electric field on orbital memory, we studied the distance-dependent influence of an atomic Cu donor on the state favorability of an individual Co atom on black phosphorus. Using low temperature scanning tunneling microscopy and spectroscopy, we characterized the electronic properties of individual Cu donors, corroborating this behavior with ab initio calculations based on density functional theory. We studied the influence of an individual donor on the charging energy and stochastic behavior of an individual Co atom. We found a strong impact on the state favorability in the stochastic limit. These findings provide quantitative information about the influence of local electric fields on atomic orbital memory.
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Affiliation(s)
- Elze J Knol
- Institute for Molecules and Materials, Radboud University, Nijmegen 6525AJ, Netherlands
| | - Brian Kiraly
- Institute for Molecules and Materials, Radboud University, Nijmegen 6525AJ, Netherlands
| | - Alexander N Rudenko
- Institute for Molecules and Materials, Radboud University, Nijmegen 6525AJ, Netherlands
| | | | - Mikhail I Katsnelson
- Institute for Molecules and Materials, Radboud University, Nijmegen 6525AJ, Netherlands
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