Intermediate-Level Synthesis of a Gauss-Jordan Elimination Linear Solver
Bibliographic record
Abstract
As the world of computing goes more and more parallel, reconfigurable computing can enable interesting compromises in terms of processing speed and power consumption between CPUs and GPUs. Yet, from a developer's perspective, programming Field-Programmable Gate Arrays to implement application specific processors still represents a significant challenge. In this paper, we present the application of an Intermediate-Level Synthesis methodology to the design of a Gauss-Jordan elimination linear solver on FPGA. The ILS methodology takes for input a language offering an Algorithmic-State Machine programming model. Each ASM handles blocking and non-blocking connections between data-synchronized channels having streaming interfaces with implicit ready-to-send/receive signals. Using our compiler, a scalable linear solver design reaching as much as 46.2 GFLOPS was designed and tested in a matter of days, showing how the ILS methodology can enable an interesting design time/performance compromise between RTL and HLS methodologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".