HLS-based FPGA Acceleration of Light Propagation Simulation in Turbid Media
Bibliographic record
Abstract
Several clinical applications rely on understanding light transport in heterogeneous biological tissues. Researchers usually resort to Monte-Carlo (MC) simulations to model the problem accurately. However, MC simulations require acceleration for better turnaround time, and this motivates the use of Field-Programmable Gate Arrays (FPGAs) to accelerate the algorithm. Nevertheless, the long cycle of developing and verifying FPGA designs makes it challenging to model realistic tissues accurately and smoothly. To this end, we present a complete and highly-optimized MC simulator for light propagation in 3D voxel-based biological tissue representations with floating-point operations using High-Level Synthesis (HLS). We provide practical guidelines in utilizing HLS to create efficient structures that help achieve the desired throughput. We also show where future work is needed to improve HLS. We use Vivado to implement the design on a Xilinx Kintex Ultrascale FPGA running at 150 MHz. With a design time of 1.5 months, experimental results show a 3x speedup against the fastest software simulator published to date.
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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) | 0.000 | 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) | 0.007 | 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".