A Probabilistic Compute Fabric Based on Coupled Ring Oscillators for Solving Combinatorial Optimization Problems

Ahmed, Ibrahim; Chiu, Po-Wei; Moy, William; Kim, Chris H.

doi:10.1109/jssc.2021.3062821

Cited by 66 publications

(36 citation statements)

References 26 publications

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“…Oscillator networks have been experimentally demonstrated with various systems such as bulk analog electronic 95,[99][100][101] , VO 2 insulator-to-metal transition 31,102,103 (Fig. 2c), spin 104,105 and integrated CMOS electronic [106][107][108] oscillators to solve problems such as graph coloring, maximum independent set, and the Ising model. In several of these studies, by adding noise and turning on the interactions smoothly, it was found that this enabled the network to find low-energy solutions of the Ising model.…”

Section: Oscillator-based Computingmentioning

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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Section: Oscillator-based Computingmentioning

confidence: 99%

Ising machines as hardware solvers of combinatorial optimization problems

Mohseni¹,

McMahon²,

Byrnes³

2022

Preprint

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“…In parallel, different analog couplings have been proposed using resistors and capacitors (Csaba and Porod, 2020). In literature, (Ahmed et al, 2021) reported a fully coupled ONN implementation of 100 neurons. It is, to the best of our knowledge, the largest implementation of an ONN with fully coupled oscillators.…”

Section: Onn Biological Inspiration and Related Workmentioning

confidence: 99%

Digital Implementation of Oscillatory Neural Network for Image Recognition Applications

et al. 2021

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Computing paradigm based on von Neuman architectures cannot keep up with the ever-increasing data growth (also called “data deluge gap”). This has resulted in investigating novel computing paradigms and design approaches at all levels from materials to system-level implementations and applications. An alternative computing approach based on artificial neural networks uses oscillators to compute or Oscillatory Neural Networks (ONNs). ONNs can perform computations efficiently and can be used to build a more extensive neuromorphic system. Here, we address a fundamental problem: can we efficiently perform artificial intelligence applications with ONNs? We present a digital ONN implementation to show a proof-of-concept of the ONN approach of “computing-in-phase” for pattern recognition applications. To the best of our knowledge, this is the first attempt to implement an FPGA-based fully-digital ONN. We report ONN accuracy, training, inference, memory capacity, operating frequency, hardware resources based on simulations and implementations of 5 × 3 and 10 × 6 ONNs. We present the digital ONN implementation on FPGA for pattern recognition applications such as performing digits recognition from a camera stream. We discuss practical challenges and future directions in implementing digital ONN.

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“…There are approaches such as Memcomputing [16,17], chaotic dynamical systems [18][19][20], and a number of approaches based on coupled bistable dynamical elements, such as stochastic magnetic bits [21,22] or coupled oscillators. Many types of coupled oscillators have been proposed: laser parametric oscillators [23][24][25], injection-locked LC oscillators [26,27], CMOS ring oscillators [28], phase-transition oscillators [29], coupled multicore fiber lasers [30], and coupled polaritonic cavities [31]. Each oscillator is forced into bistability by a physical nonlinearity, and chooses one of the two stable states based on the strengths of its interactions with other oscillators.…”

Section: Introductionmentioning

confidence: 99%

Equivalence of coupled parametric oscillator dynamics to Lagrange multiplier primal-dual optimization

Vadlamani¹,

Xiao²,

Yablonovitch³

2022

Preprint

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There has been a recent surge of interest in physics-based solvers for combinatorial optimization problems. We present a dynamical solver for the Ising problem that is comprised of a network of coupled parametric oscillators and show that it implements Lagrange multiplier constrained optimization. We show that the pump depletion effect, which is intrinsic to parametric oscillators, enforces binary constraints and enables the system's continuous analog variables to converge to the optimal binary solutions to the optimization problem. Moreover, there is an exact correspondence between the equations of motion for the coupled oscillators and the update rules in the primal-dual method of Lagrange multipliers. Though our analysis is performed using electrical LC oscillators, it can be generalized to any system of coupled parametric oscillators. We simulate the dynamics of the coupled oscillator system and demonstrate that the performance of the solver on a set of benchmark problems is comparable to the best-known results obtained by digital algorithms in the literature.

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A Probabilistic Compute Fabric Based on Coupled Ring Oscillators for Solving Combinatorial Optimization Problems

Cited by 66 publications

References 26 publications

Ising machines as hardware solvers of combinatorial optimization problems

Ising machines as hardware solvers of combinatorial optimization problems

Digital Implementation of Oscillatory Neural Network for Image Recognition Applications

Equivalence of coupled parametric oscillator dynamics to Lagrange multiplier primal-dual optimization

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