An Efficient Simulated Oscillator-Based Ising Machine on FPGAs
Bibliographic record
Abstract
Ising model-based computing has emerged as an efficient approach for solving combinatorial optimization problems. In particular, oscillator-based Ising machines (OIMs) have shown promising performance in such metrics as solution quality, time-to-solution, and energy-to-solution. Existing designs, however, require customized chips and use sparsely connected topologies with limited precision for the spin coupling coefficients. In this paper, a simulated oscillator-based Ising machine (SOIM) is designed with a fully connected topology and high coefficient precision for an efficient implementation on field-programmable gate arrays (FPGAs). To this end, an FPGA-oriented model is first developed to describe the underlying mechanism of an oscillator-based Ising machine based on revised differential equations. To save hardware, periodic functions that are expensive to implement are replaced by piece-wise linear functions. Moreover, Gaussian noise is omitted in the system to further save hardware. An OIM simulation algorithm is then proposed to solve the new differential equations using the Euler integration method. From experiments on solving 800-node max-cut problems, the results reach a level of 99% of the best-known values. SOIMs of different sizes are then developed and synthesized on a Zynq UltraScale+ board. Compared with state-of-the-art FPGA-based Ising machines, the SOIM is expected to utilize fewer hardware resources to efficiently solve complex combinatorial optimization problems by leveraging a high coefficient precision and a fully connected topology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".