A thermodynamic core using voltage-controlled spin–orbit-torque magnetic tunnel junctions

Lee, Albert; Dai, Bingqian; Wu, Di; Wu, Hao; Schwartz, Robert N.; Wang, Kang L.

doi:10.1088/1361-6528/abeb9b

Cited by 5 publications

(2 citation statements)

References 45 publications

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“…While emphasizing the use of probabilistic magnetic bits (“p-bits”) as key enabling elements, [ 44 , 45 , 46 , 47 ] it is possible to extend these ideas to applications of invertible logic and integer factorization in probabilistic analog hardware, digital hardware, and simulation. Lee [ 48 ] explores more complex magnetic systems, which they call “magnetic thermodynamic cores”. The key task for all these Ising/Boltzmann Machines is to rapidly equilibrate toward the ground state of an Ising model given a network of coupling coefficients corresponding to a problem of interest.…”

Section: Discussionmentioning

confidence: 99%

Thermodynamic State Machine Network

Hylton

2022

Entropy

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We describe a model system—a thermodynamic state machine network—comprising a network of probabilistic, stateful automata that equilibrate according to Boltzmann statistics, exchange codes over unweighted bi-directional edges, update a state transition memory to learn transitions between network ground states, and minimize an action associated with fluctuation trajectories. The model is grounded in four postulates concerning self-organizing, open thermodynamic systems—transport-driven self-organization, scale-integration, input-functionalization, and active equilibration. After sufficient exposure to periodically changing inputs, a diffusive-to-mechanistic phase transition emerges in the network dynamics. The evolved networks show spatial and temporal structures that look much like spiking neural networks, although no such structures were incorporated into the model. Our main contribution is the articulation of the postulates, the development of a thermodynamically motivated methodology addressing them, and the resulting phase transition. As with other machine learning methods, the model is limited by its scalability, generality, and temporality. We use limitations to motivate the development of thermodynamic computers—engineered, thermodynamically self-organizing systems—and comment on efforts to realize them in the context of this work. We offer a different philosophical perspective, thermodynamicalism, addressing the limitations of the model and machine learning in general.

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Section: Discussionmentioning

confidence: 99%

Thermodynamic State Machine Network

Hylton

2022

Entropy

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“…When implemented on dedicated hardware, this provides the chance to exploit the parallelization of digital hardware accelerators and analog computing. For the analog computation approach, numerous physical implementations of Ising and related models have been realized or proposed, including magnetic devices 29,[44][45][46][47][48][49][50][51] , optics 34,52,53 , memristors 30,54 , spinswitches 55 , quantum dots 56 , single atoms 33 , microdroplets 57 , and Bose-Einstein condensates 24,58 (see Fig. 2).…”

Section: Operating Principles Of Ising Machinesmentioning

confidence: 99%

Ising machines as hardware solvers of combinatorial optimization problems

Mohseni¹,

McMahon²,

Byrnes³

2022

Preprint

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Ising machines are hardware solvers which aim to find the absolute or approximate ground states of the Ising model. The Ising model is of fundamental computational interest because it is possible to formulate any problem in the complexity class NP as an Ising problem with only polynomial overhead. A scalable Ising machine that outperforms existing standard digital computers could have a huge impact for practical applications for a wide variety of optimization problems. In this review, we survey the current status of various approaches to constructing Ising machines and explain their underlying operational principles. The types of Ising machines considered here include classical thermal annealers based on technologies such as spintronics, optics, memristors, and digital hardware accelerators; dynamical-systems solvers implemented with optics and electronics; and superconducting-circuit quantum annealers. We compare and contrast their performance using standard metrics such as the ground-state success probability and time-to-solution, give their scaling relations with problem size, and discuss their strengths and weaknesses. Key points• Dedicated hardware solvers for the Ising model are of great interest due to the many potential practical applications and the end of Moore's law which motivate alternative computational approaches.• Three main computing methods that Ising machines employ are classical annealing, quantum annealing, and dynamical system evolution. A single machine can operate based on multiple computing approaches.• Today, Ising hardware based on classical digital technologies are the best performing for common benchmark problems. However, the performance is problem dependent and alternative methods can perform well for particular classes of problems.• For particular crafted problem instances, quantum approaches have been observed to have a superior performance over classical algorithms, motivating quantum hardware approaches and quantum-inspired classical algorithms.• Hybrid quantum-classical and digital-analog algorithms are promising for future development; they may harness the complementary advantages of both.

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