Massively parallel systolic‐array architectures for 2d IIR polyphase space–time plane‐wave beam digital filters
Bibliographic record
Abstract
SUMMARY A systolic architecture has recently been proposed for implementing two‐dimensional infinite impulse response (IIR) space–time beam plane‐wave filters at a throughput of one‐frame‐per‐clock–cycle for such applications as real‐time broadband smart antennas. A novel polyphase systolic architecture is proposed here that further increases the throughput of these IIR beam filters, by a factor of M, to M‐frames‐per‐clock‐cycle, where M is the number of polyphases. The proposed method combines the polyphase approach, along with pipelining and look‐ahead optimization methods, to achieve frame sample frequencies that are several times higher than the clock‐cycle limit of the very large‐scale integration (VLSI) technology, thereby potentially allowing multi‐GHz frame sample frequencies using current custom VLSI circuits. The implementation of a field programmable gate array‐based real‐time prototype is described, tested and verified for the two‐phase case (M= 2) at a technology‐limited clock frequency of 50 MHz which corresponds to a throughput of 100 million‐frames‐per‐clock–cycle. Copyright © 2010 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.002 | 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".