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Record W1975949393 · doi:10.5555/2840819.2840853

SAT Solving using FPGA-based Heterogeneous Computing

2015· article· en· W1975949393 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Conference on Computer Aided Design · 2015
Typearticle
Languageen
FieldComputer Science
TopicFormal Methods in Verification
Canadian institutionsMcMaster University
Fundersnot available
KeywordsField-programmable gate arrayComputer scienceBoolean satisfiability problemSoftwareThroughputParallel computingSolverComputationSymmetric multiprocessor systemComputer architectureReconfigurable computingGate arrayEmbedded systemTheoretical computer scienceAlgorithmOperating systemProgramming language

Abstract

fetched live from OpenAlex

We present a heterogeneous computing solution to the Boolean satisfiability (SAT) problem. Our field-programmable gate array (FPGA)-based implementation for accelerating the common case computation within a SAT solver utilizes most of the FPGA resources and it seamlessly integrates with our software host. Algorithms and data structures were redesigned to maximize the strengths of customized computing and generalizable optimizations are proposed to maximize throughput, minimize communication latencies, and compact hardware memory. We are significantly faster than state-of-the-art SAT solvers in software and hardware.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.454
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.290
GPT teacher head0.373
Teacher spread0.083 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it