A Reconfigurable Systolic Array Architecture for Multicarrier Wireless and Multirate Applications
Bibliographic record
Abstract
A reconfigurable systolic array (RSA) architecture that supports the realization of DSP functions for multicarrier wireless and multirate applications is presented. The RSA consists of coarse‐grained processing elements that can be configured as complex DSP functions that are the basic building blocks of Polyphase‐FIR filters, phase shifters, DFTs, and Polyphase‐DFT circuits. The homogeneous characteristic of the RSA architecture, where each reconfigurable processing element (PE) cell is connected to its nearest neighbors via configurable switch (SW) elements, enables array expansion for parallel processing and facilitates time sharing computation of high‐throughput data by individual PEs. For DFT circuit configurations, an algorithmic optimization technique has been employed to reduce the overall number of vector‐matrix products to be mapped on the RSA. The hardware complexity and throughput of the RSA‐based DFT structures have been evaluated and compared against several conventional modular FFT realizations. Designs and circuit implementations of the PE cell and several RSAs configured as DFT and Polyphase filter circuits are also presented. The RSA architecture offers significant flexibility and computational capacity for applications that require real time reconfiguration and high‐density computing.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".