MAQO: A Scalable Many-Core Annealer for Quadratic Optimization on a Stratix 10 FPGA
Bibliographic record
Abstract
Quadratic Assignment Problems are a class of NP-hard combinatorial optimization problems with a wide range of real-world applications such as Vehicle Routing and FPGA Block Placement. Despite technological advances, solvers that target Quadratic Assignment Problems still require significant computing resources and time, especially as problem sizes grow; with the end of Dennard Scaling leading to the increased development of domain-specific hardware for such tasks. This paper presents MAQO: a hardware architecture for a scalable many-core annealer for quadratic optimization, implemented on an Intel Stratix 10 FPGA. MAQO is comprised of 32 domain-specific processing cores, supporting Quadratic Assignment Problems with up to 200 integer variables, in combination with a Parallel Tempering controller, all operating at 220MHz with a maximum FPGA power draw of 40W. We benchmark MAQO's performance, via solving some of the most difficult known Quadratic Assignment Problem instances, and show that it is, on average, 2.5 times faster than the next best competing solver with 22 times better performance-per-watt.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".