Replica Exchange MCMC Hardware With Automatic Temperature Selection and Parallel Trial
Bibliographic record
Abstract
A replica exchange Markov Chain Monte Carlo (MCMC) engine is developed with automatic temperature adjustment for solving combinatorial optimization problems by minimizing the energy of the Ising model. The automatic temperature adjustment scheme ensures that the MCMC process is optimized at every stage of the execution. This approach is performed by dynamically adjusting temperatures of all replicas, based on the properties of any given problem, in addition to the capability of automatically inserting new replicas or removing any existing replicas to achieve the best possible resource efficiency and execution time. The proposed algorithm is integrated with parallel evaluation of energy increment and update scheme. The engine is implemented on the FPGA platform with a capacity of running up to 42 replicas in pipeline, each running 1024 fully-connected Ising spins in parallel. The performance of the hardware is examined with three different classes of problems, Vertex Cover, Maximum-Cut, and Travelling Salesman using the engine in three modes, simulated annealing, with replica exchange while the adjustments are turned on or off. Up to 16x speedup is observed by turning on the replica exchange capability in addition to the advantage of eliminating the challenging process of finding an optimal annealing schedule for simulated annealing process.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".