Scalable, High Performance Fourier Domain Optical Coherence Tomography: Why FPGAs and Not GPGPUs
Bibliographic record
Abstract
Fourier Domain Optical Coherence Tomography (FD-OCT) is an emerging biomedical imaging technology featuring ultra-high resolution and fast imaging speed. Due to the complexity of the FD-OCT algorithm, real time FD-OCT imaging demands high performance computing platforms. However, the scaling of real-time FD-OCT processing for increasing data acquisition rates and 3-dimensional (3D) imaging is quickly outpacing the performance of general purpose processors. Our research analyzes the scalability of accelerating FD-OCT processing on two potential implementation platforms: General Purpose Graphical Processing Units (GPGPUs) and Field Programmable Gate Arrays (FPGAs). We implemented a complete FD-OCT system using a NVIDIA GPGPU as co-processor, with a speed up of 6.9x over general purpose processors (GPPs). We also created a hardware processing engine using FPGAs with a speed up of 15.5x over GPPs for a single pipeline, which can be replicated to further increase performance. Our analysis of the performance and scalability for both platforms shows that, while GPGPUs offer an easy and low cost solution for accelerating FD-OCT, FPGAs are more likely to match the long term demands for real-time, 3D, FD-OCT imaging.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".