Optimizing FPGA-based Sparse Coding for Real-time Processing in X-ray Instrumentation
Bibliographic record
Abstract
As detectors improve, large physics experiments generate more data. However, real-time raw data processing near the sensors can be challenging due to the high data velocity and the resource-constrained location. A practical solution involves using low-latency data reduction to lower the incoming throughput. Sparse representations and machine learning are popular methods for data reduction, with the Learned Iterative Shrinkage-Thresholding Algorithm, dubbed LISTA, combining both techniques to produce fast approximations of sparse coding. In previous work, we proposed a multi-stage strategy to compress the data at the edge. We targeted the billion-pixel X-ray camera, an X-ray imaging system with a data rate of 10 TB/s. The compression leverages a LISTA-like architecture followed by quantization and entropy coding, achieving a 100:1 compression ratio with minimal image artifacts. Using high-level synthesis, we fitted the fully connected LISTA on an FPGA. Although the inference time was around 1 μs, the number of parameters per layer of the network was limited, imposing a low input size and restricting the FPGA throughput. In this work, we explore and compare solutions for a larger input size that maintain similar performance. We studied splitting the initial linear layers into smaller ones using low-rank matrix factorization. Preliminary results indicate that the factorization can achieve a 77% increase in the input size, with increased throughput at the cost of added resources. Finally, we discuss the current limitations of the compression strategy and potential improvements moving forward.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".