Sequential spin-logic algorithmics for Ising machines

Ising machines are considered as promising hardware solvers. The idea is to map the solution of a problem to the ground state of an Ising spin glass and find this minimum by performing annealing in an Ising machine that emulates spin physics. However, previous approaches to deterministic sequential algorithmics have been plagued by the hiearchy problem: couplings become exponentially small along the causal direction. Here, we present a method to map sequential algorithms to an Ising problem with only a linear decay of the couplings and illustrate this with n-bit binary number adders. The resulting computing system is thus scalable, opening a way for efficient Ising machines for sequential algorithms.Ising machines are considered as promising hardware solvers. The idea is to map the solution of a problem to the ground state of an Ising spin glass and find this minimum by performing annealing in an Ising machine that emulates spin physics. However, previous approaches to deterministic sequential algorithmics have been plagued by the hiearchy problem: couplings become exponentially small along the causal direction. Here, we present a method to map sequential algorithms to an Ising problem with only a linear decay of the couplings and illustrate this with n-bit binary number adders. The resulting computing system is thus scalable, opening a way for efficient Ising machines for sequential algorithms.

Physical review. EMay 29, 20250 citations

Efficient optimization accelerator framework for multi-state spin Ising problems

Chirag Garg, Sayeef Salahuddin

Abstract Ising Machines are emerging hardware architectures that efficiently solve NP-hard combinatorial optimization problems. Generally, combinatorial problems are transformed into quadratic unconstrained binary optimization (QUBO) form, but this transformation often complicates the solution landscape, degrading performance, especially for multi-state problems. To address this challenge, we model spin interactions as generalized boolean logic function to significantly reduce the exploration space. We demonstrate the effectiveness of our approach on graph coloring problem using probabilistic Ising solvers, achieving similar accuracy compared to state-of-the-art heuristics and machine learning algorithms. It also shows significant improvement over state-of-the-art QUBO-based Ising solvers, including probabilistic Ising and simulated bifurcation machines. We also design 1024-neuron all-to-all connected probabilistic Ising accelerator on FPGA with the proposed approach that shows $\sim$ ~ 10000 $\times$ × performance acceleration compared to GPU-based Tabucol heuristics and reducing physical neurons by 1.5 $-$ − 4 $\times$ × over baseline Ising frameworks. Thus, this work establishes superior efficiency, scalability and solution quality for multi-state optimization problems.Abstract Ising Machines are emerging hardware architectures that efficiently solve NP-hard combinatorial optimization problems. Generally, combinatorial problems are transformed into quadratic unconstrained binary optimization (QUBO) form, but this transformation often complicates the solution landscape, degrading performance, especially for multi-state problems. To address this challenge, we model spin interactions as generalized boolean logic function to significantly reduce the exploration space. We demonstrate the effectiveness of our approach on graph coloring problem using probabilistic Ising solvers, achieving similar accuracy compared to state-of-the-art heuristics and machine learning algorithms. It also shows significant improvement over state-of-the-art QUBO-based Ising solvers, including probabilistic Ising and simulated bifurcation machines. We also design 1024-neuron all-to-all connected probabilistic Ising accelerator on FPGA with the proposed approach that shows $$\sim$$ ~ 10000 $$\times$$ × performance acceleration compared to GPU-based Tabucol heuristics and reducing physical neurons by 1.5 $$-$$ − 4 $$\times$$ × over baseline Ising frameworks. Thus, this work establishes superior efficiency, scalability and solution quality for multi-state optimization problems.

Nature CommunicationsOct 30, 20253 citations

Ising machines as hardware solvers of combinatorial optimization problems

Naeimeh Mohseni, Peter L. McMahon, Tim Byrnes

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.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.

arXiv:2204.00276Apr 1, 20220 citations

Efficient Optimization Accelerator Framework for Multistate Ising Problems

Chirag Garg, Sayeef Salahuddin

Ising Machines are emerging hardware architectures that efficiently solve NP-Hard combinatorial optimization problems. Generally, combinatorial problems are transformed into quadratic unconstrained binary optimization (QUBO) form, but this transformation often complicates the solution landscape, degrading performance, especially for multi-state problems. To address this challenge, we model spin interactions as generalized boolean logic function to significantly reduce the exploration space. We demonstrate the effectiveness of our approach on graph coloring problem using probabilistic Ising solvers, achieving similar accuracy compared to state-of-the-art heuristics and machine learning algorithms. It also shows significant improvement over state-of-the-art QUBO-based Ising solvers, including probabilistic Ising and simulated bifurcation machines. We also design 1024-neuron all-to-all connected probabilistic Ising accelerator on FPGA with the proposed approach that shows ~10000x performance acceleration compared to GPU-based Tabucol heuristics and reducing physical neurons by 1.5-4x over baseline Ising frameworks. Thus, this work establishes superior efficiency, scalability and solution quality for multi-state optimization problems.Ising Machines are emerging hardware architectures that efficiently solve NP-Hard combinatorial optimization problems. Generally, combinatorial problems are transformed into quadratic unconstrained binary optimization (QUBO) form, but this transformation often complicates the solution landscape, degrading performance, especially for multi-state problems. To address this challenge, we model spin interactions as generalized boolean logic function to significantly reduce the exploration space. We demonstrate the effectiveness of our approach on graph coloring problem using probabilistic Ising solvers, achieving similar accuracy compared to state-of-the-art heuristics and machine learning algorithms. It also shows significant improvement over state-of-the-art QUBO-based Ising solvers, including probabilistic Ising and simulated bifurcation machines. We also design 1024-neuron all-to-all connected probabilistic Ising accelerator on FPGA with the proposed approach that shows ~10000x performance acceleration compared to GPU-based Tabucol heuristics and reducing physical neurons by 1.5-4x over baseline Ising frameworks. Thus, this work establishes superior efficiency, scalability and solution quality for multi-state optimization problems.

arXiv:2505.20250May 26, 20250 citations

HETRI: Heterogeneous Ising Multiprocessing

Ahmet Efe, Hüsrev Cılasun, Abhimanyu Kumar +4 more

Ising machines are effective solvers for complex combinatorial optimization problems. The idea is mapping the optimal solution(s) to a combinatorial optimization problem to the minimum energy state(s) of a physical system, which naturally converges to a minimum energy state upon perturbance. The underlying mathematical abstraction, the Ising model, can capture the dynamic behavior of different physical systems by mapping each problem variable to a spin which can interact with other spins. Ising model as a mathematical abstraction can be mapped to hardware using traditional devices. In this paper we instead focus on Ising machines which represent a network of physical spins directly implemented in hardware using, e.g., quantum bits or electronic oscillators. To eliminate the scalability bottleneck due to the mismatch in problem vs. Ising machine size and connectivity, in this paper we make the case for HETRI: Heterogeneous Ising Multiprocessing. HETRI organizes the maximum number of physical spins that the underlying technology supports in Ising cores; and multiple independent Ising cores, in Ising chips. Ising cores in a chip feature different inter-spin connectivity or spin counts to match the problem characteristics. We provide a detailed design space exploration and quantify the performance in terms of time or energy to solution and solution accuracy with respect to homogeneous alternatives under the very same hardware budget and considering the very same spin technology.Ising machines are effective solvers for complex combinatorial optimization problems. The idea is mapping the optimal solution(s) to a combinatorial optimization problem to the minimum energy state(s) of a physical system, which naturally converges to a minimum energy state upon perturbance. The underlying mathematical abstraction, the Ising model, can capture the dynamic behavior of different physical systems by mapping each problem variable to a spin which can interact with other spins. Ising model as a mathematical abstraction can be mapped to hardware using traditional devices. In this paper we instead focus on Ising machines which represent a network of physical spins directly implemented in hardware using, e.g., quantum bits or electronic oscillators. To eliminate the scalability bottleneck due to the mismatch in problem vs. Ising machine size and connectivity, in this paper we make the case for HETRI: Heterogeneous Ising Multiprocessing. HETRI organizes the maximum number of physical spins that the underlying technology supports in Ising cores; and multiple independent Ising cores, in Ising chips. Ising cores in a chip feature different inter-spin connectivity or spin counts to match the problem characteristics. We provide a detailed design space exploration and quantify the performance in terms of time or energy to solution and solution accuracy with respect to homogeneous alternatives under the very same hardware budget and considering the very same spin technology.

arXiv:2410.23517Oct 30, 20240 citations

A Fully-Connected Ising Model Embedding Method and Its Evaluation for CMOS Annealing Machines

Daisuke Oku, Kotaro Terada, Masato HAYASHI +3 more

Combinatorial optimization problems with a large solution space are difficult to solve just using von Neumann computers. Ising machines or annealing machines have been developed to tackle these problems as a promising Non-von Neumann computer. In order to use these annealing machines, every combinatorial optimization problem is mapped onto the physical Ising model, which consists of spins, interactions between them, and their external magnetic fields. Then the annealing machines operate so as to search the ground state of the physical Ising model, which corresponds to the optimal solution of the original combinatorial optimization problem. A combinatorial optimization problem can be firstly described by an ideal fully-connected Ising model but it is very hard to embed it onto the physical Ising model topology of a particular annealing machine, which causes one of the largest issues in annealing machines. In this paper, we propose a fully-connected Ising model embedding method targeting for CMOS annealing machine. The key idea is that the proposed method replicates every logical spin in a fully-connected Ising model and embeds each logical spin onto the physical spins with the same chain length. Experimental results through an actual combinatorial problem show that the proposed method obtains spin embeddings superior to the conventional de facto standard method, in terms of the embedding time and the probability of obtaining a feasible solution.Combinatorial optimization problems with a large solution space are difficult to solve just using von Neumann computers. Ising machines or annealing machines have been developed to tackle these problems as a promising Non-von Neumann computer. In order to use these annealing machines, every combinatorial optimization problem is mapped onto the physical Ising model, which consists of spins, interactions between them, and their external magnetic fields. Then the annealing machines operate so as to search the ground state of the physical Ising model, which corresponds to the optimal solution of the original combinatorial optimization problem. A combinatorial optimization problem can be firstly described by an ideal fully-connected Ising model but it is very hard to embed it onto the physical Ising model topology of a particular annealing machine, which causes one of the largest issues in annealing machines. In this paper, we propose a fully-connected Ising model embedding method targeting for CMOS annealing machine. The key idea is that the proposed method replicates every logical spin in a fully-connected Ising model and embeds each logical spin onto the physical spins with the same chain length. Experimental results through an actual combinatorial problem show that the proposed method obtains spin embeddings superior to the conventional de facto standard method, in terms of the embedding time and the probability of obtaining a feasible solution.

IEICE Transactions on Information and SystemsAug 31, 201933 citations

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~ 10000

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× performance acceleration compared to GPU-based Tabucol heuristics and reducing physical neurons by 1.5

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− 4

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× over baseline Ising frameworks. Thus, this work establishes superior efficiency, scalability and solution quality for multi-state optimization problems.