Conflict-Free Block Pipelining for FPGA-Accelerated Stochastic Simulated Bifurcation on Dense Ising Models
Bibliographic record
Abstract
The Ising model provides an efficient framework for solving combinatorial optimization problems. Stochastic simulated bifurcation (sSB) is a parallel numerical method that can rapidly find solutions to the Ising model. However, stochastic bit operations in sSB are not inherently supported by general-purpose CPU or G PU architectures. In contrast, the fine-grained reconfigurable structure of FPGAs makes it well suited for implementing stochastic bit operations and enabling large-scale parallel updates of spin states. This paper presents an FPGA-based sSB architecture that fully exploits the strengths of FPGAs through three innovations: bit-width-optimized stochastic arith-metic, symmetric matrix compression, and conflict-free block traversal. As a result, the proposed architecture achieves high performance on a single FPGA chip for a 2000-node max-cut problem, with reduced data movement and improved computational efficiency. On a Xilinx XC7Z020 FPGA operating at 71 MHz and 1.6 W, the solution reaches 99 % of the optimal cut value with >50% probability in 1.1 ms.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".