HETRI: Heterogeneous Ising Multiprocessing
H\"usrev C{\i}lasun, Abhimanyu Kumar, Ziqing Zeng, Nafisa Sadaf Prova,, Sachin S. Sapatnekar, Ulya R. Karpuzcu

TL;DR
HETRI introduces a heterogeneous multiprocessing approach for Ising machines, organizing multiple diverse Ising cores to improve scalability and performance in solving complex optimization problems.
Contribution
This paper proposes HETRI, a novel heterogeneous Ising multiprocessing architecture that enhances scalability and performance by organizing multiple diverse Ising cores.
Findings
HETRI improves solution time and energy efficiency.
Heterogeneous cores match problem characteristics better.
Performance surpasses homogeneous alternatives under same hardware budget.
Abstract
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…
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Taxonomy
TopicsComputability, Logic, AI Algorithms · Quantum Computing Algorithms and Architecture · Cellular Automata and Applications
