A multi-FPGA application-specific architecture for accelerating a floating point Fourier Integral Operator
Bibliographic record
Abstract
Many complex systems require the use of floating point arithmetic that is exceedingly time consuming to perform on personal computers. However, floating point operators are also hardware resource intensive and require longer latencies than fixed point operators to complete. Due to the reduced logic density of FPGAs relative to ASICs, it is often only possible to accelerate a portion of a floating point application in hardware. This paper presents an application-specific architecture for the hardware acceleration of a complete Fourier Integral Operator (FIO) kernel used in seismic imaging on a multi-FPGA platform. The design utilizes several floating point computing elements (CEs) to calculate the FIO kernel in parallel stages on multiple FPGAs. A detailed study of floating point CEs, including a Fast Fourier Transform (FFT) CE, and a complete FIO prototype implementation on the BEE2 platform is described. The prototype implementation has a 12.4x increase in throughput over an optimized software implementation, and a predicted 15.8x increase in throughput on the BEE3 platform.
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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 | 0.000 | 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 | 0.001 | 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".