Efficient Power-and Area-Optimized 800-Spin Ising Chip for Solving Combinatorial Optimization Problems Using Multi-Run Decremental Annealing

This paper presents a very-large-scale-integration (VLSI)-based multi-run decremental annealing (MRDA) accelerator for solving combinatorial optimization problems (COPs). The proposed Ising chip, which features 800 spins, utilizes MRDA to minimize hardware area while maintaining high accuracy. The proposed method utilizes a local energy calculation approach, which replaces total energy computations and effectively reduces redundancy in such fully connected models. The multi-threaded design improves speed while lowering power and area overheads. Extensive tests on the Gset dataset and Max-Cut problems demonstrate that the proposed method achieves rapid convergence to near-optimal solutions, ensuring both high accuracy and low energy consumption. Fabricated using Taiwan Semiconductor Manufacturing Company (TSMC) 90 nm process the chip runs at 91MHz, with a core area of 2.4mm2 and power consumption of 9.4mW. This design demonstrates superior efficiency and accuracy in solving complex COPs.

Authors

Institutions

Publication Details

Journal
IEEE Internet of Things Journal
Published
2025-01-01
DOI
https://doi.org/10.1109/jiot.2025.3595927
Primary Topic
Quantum Computing Algorithms and Architecture
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Efficient Power-and Area-Optimized 800-Spin Ising Chip for Solving Combinatorial Optimization Problems Using Multi-Run Decremental Annealing

Yi‐Hao Yen, Chin-Fu Nien, Yuan‐Ho Chen, Chao‐Sung Lai
IEEE Internet of Things Journal
Quantum Computing Algorithms and Architecture
article

Efficient Power-and Area-Optimized 800-Spin Ising Chip for Solving Combinatorial Optimization Problems Using Multi-Run Decremental Annealing

Yi‐Hao Yen, Chin-Fu Nien, Yuan‐Ho Chen, Chao‐Sung Lai
article en

Abstract

This paper presents a very-large-scale-integration (VLSI)-based multi-run decremental annealing (MRDA) accelerator for solving combinatorial optimization problems (COPs). The proposed Ising chip, which features 800 spins, utilizes MRDA to minimize hardware area while maintaining high accuracy. The proposed method utilizes a local energy calculation approach, which replaces total energy computations and effectively reduces redundancy in such fully connected models. The multi-threaded design improves speed while lowering power and area overheads. Extensive tests on the Gset dataset and Max-Cut problems demonstrate that the proposed method achieves rapid convergence to near-optimal solutions, ensuring both high accuracy and low energy consumption. Fabricated using Taiwan Semiconductor Manufacturing Company (TSMC) 90 nm process the chip runs at 91MHz, with a core area of 2.4mm2 and power consumption of 9.4mW. This design demonstrates superior efficiency and accuracy in solving complex COPs.

IEEE Internet of Things Journal
Chang Gung University of Science and Technology (TW), National Yang Ming Chiao Tung University (TW), National Taiwan University of Science and Technology (TW), Chang Gung University (TW)
National Science and Technology Council
Affordable and clean energy
Openalex Percentile: Top 90%
Quantum Computing Algorithms and Architecture
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.