FPGA Implementation of an Improved OMP for Compressive Sensing Reconstruction
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Bibliographic record
Abstract
This article proposes an improved orthogonal matching pursuit (OMP) algorithm and its implementation with Xilinx Vivado high-level synthesis (HLS). We use the Gram-Schmidt orthogonalization to improve the update process of signal residuals so that the signal recovery only needs to perform the least-squares solution once, which greatly reduces the number of matrix operations in a hardware implementation. Simulation results show that our OMP algorithm has the same signal reconstruction accuracy as the original OMP algorithm. Our approach provides a fast and reconfigurable implementation for different signal sizes, different measurement matrix sizes, and different sparsity levels. The proposed design can recover a 128-length signal with measurement number M = 32 and sparsity K = 5 and K = 8 in 13.2 and 21 μs, which is at least a 21.9% and 22.2% improvement compared with the existing HLS-based works; a 256-length signal with M = 64 and K = 8 in 20.6 μs, which is a 24% improvement compared with the existing work; and a 1024-length signal with measurement number M = 256 and sparsity K = 12 and K = 36 in 150.3 and 423 μs, respectively, which are close to the results of traditional hardware description language (HDL) implementations. Our results show that our improved OMP algorithm not only offers a superior reconstruction time compared with other recent HLS-based works but also can compete with existing works that are implemented using the traditional field-programmable gate array (FPGA) design route.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it