CORDIC-Based Enhanced Systolic Array Architecture for QR Decomposition
Bibliographic record
Abstract
Multiple input multiple output (MIMO) with orthogonal frequency division multiplexing (OFDM) systems typically use orthogonal-triangular (QR) decomposition. In this article, we present an enhanced systolic array architecture to realize QR decomposition based on the Givens rotation (GR) method for a 4 × 4 real matrix. The coordinate rotation digital computer (CORDIC) algorithm is adopted and modified to speed up and simplify the process of GR. To verify the function and evaluate the performance, the proposed architectures are validated on a Virtex 5 FPGA development platform. Compared to a commercial implementation of vectoring CORDIC, the enhanced vectoring CORDIC is presented that uses 37.7% less hardware resources, dissipates 71.6% less power, and provides a 1.8 times speedup while maintaining the same computation accuracy. The enhanced QR systolic array architecture based on the enhanced vectoring CORDIC saves 24.5% in power dissipation, provides a factor of 1.5-fold improvement in throughput, and the hardware efficiency is improved 1.45-fold with no accuracy penalty when compared to our previously proposed QR systolic array architecture.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.006 | 0.002 |
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".