Design of a Flexible, Energy Efficient (Auto)Correlator Block for Timing Synchronization
Bibliographic record
Abstract
Multi-mode and multi-standard connectivity has become a necessity for portable communication systems. A convenient architectural solution is to build flexible systems that can be reprogrammed to meet requirements of multiple standards. One of the major issues in this context is the resource overhead required by programmability. In particular, in the latest VLSI technology nodes, energy consumption has become a very severe problem, greatly impacting the reliability of the hardware. Therefore, any design aimed at the implementation of multi-mode multi-standard communication systems must be strictly targeted at the lowest power consumption without jeopardizing peak performance, while, at the same time, retaining a high degree of flexibility. This work presents the design and implementation of a (auto)correlator block for timing synchronization. The design is composed of a scalable computational unit, which allows to meet real-time requirements of different wireless communication standards (e.g. W-CDMA, IEEE 802.11a/g/n). Moreover, dynamic power management allows to dynamically trade-off energy consumption versus performance, adapting power dissipation to the specific requirements of each supported standard, as well as to follow dynamic variations of the computation load.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".